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Gaining insights into social factors affecting patients’ lives can be used to augment healthcare delivery in a big way.

That’s according to Dr. Courtney Lyles, an associate professor at the University of California San Francisco, who helped create an expansive social determinants of health-focused data visualization platform. She discussed the platform and its development at the Workgroup for Electronic Data Interchange’s The Quest for Health Equity virtual conference on Feb. 23.

The platform, UCSF Health Atlas, was released last April. Focused on California-based data, it is designed to enable researchers to explore neighborhood-level characteristics and see how they relate to health outcomes. It also includes data on Covid-19.

“It’s pretty clear, and Covid has made it even more clear, that health is really impacted by place,” said Lyles, who is also co-principal investigator of the UCSF Population Health Data Initiative. “Where we live, including our physical and social environment, directly influences health outcomes.”

The interactive platform includes more than 150 social determinates of health variables at different levels of granularity down to the census tract level, which includes between 1,200 and 8,000 people. The platform draws from several sources including the American Community Survey from the Census Bureau, CalEnviroScreen developed by the California Environmental Protection Agency and the California Department of Public Health.

But one of the main challenges of developing a platform like this is deciding what publicly available data to include from the massive trove that is available, said Lyles.

To create Health Atlas, UCSF relied heavily on the Health People 2020 framework created by the Office of Disease Prevention and Health Promotion. This framework helped the UCSF team think through five domains of social factors, said Lyles. These domains were:

  • Demographic characteristics
  • Socioeconomic factors
  • Community characteristics
  • Neighborhood characteristics
  • Health and healthcare indicators

The UCSF team then went in and selected useful variables within each of the five domains. For example, within the community domain, the team selected individual variables like language and foreign-born status as well as wider variables like population density and household composition — that is, people living in households with children versus single-adult households versus seniors living alone.

“Thinking about those variables has really been interesting,” said Lyles. “[It allowed us to] think through what matters for population health and health equity risk.”

Combining all this data on an interactive map enables researchers and clinicians to drill down into granular data on any one variable, and also compare different variables, she said.

UCSF has gone a step further and made it possible to link that social determinants of health data with its EHR data. For example, UCSF combined the two to gain insights into racial disparities in hypertension outcomes. Eliminating this disparity was a pre-Covid goal for the health system, Lyles said.

First, the team extracted every single address that existed within UCSF’s EHR, and then they geo-coded those addresses onto latitude and longitude. They assigned census tract identifiers to those geo-coded addresses so that they could be linked out to the publicly available datasets. Finally, they gathered clinical and demographic data for patients with hypertension receiving care within the system.

By combining all this the system was able to track hypertension patients by race, neighborhood and socioeconomic status in San Francisco on the map and compare these variables with health outcomes, Lyles said. They found that Black patients are concentrated in certain neighborhoods, and they’re struggling with hypertension control.

“This is not showing us something that perhaps we didn’t know already about structural disparities in care or structural disparities in our society,” Lyles said.

But it does show the urgent need for including place-based strategies in health systems’ disparity reduction and quality improvement programs.

“When you put it out there in a visual display, it actually gives you even more impetus to think about neighborhoods you want to target, or [places where] you might think differently about your interventions moving forward,” she said.

Photo: GarryKillian, Getty Images

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Insightin Health, a company that offers payers a personalized member engagement platform, has raised $12 million in a Series A funding round.

The round was co-led by the Blue Venture Fund — a collaboration between Blue Cross Blue Shield companies, the Blue Cross Blue Shield Association and Sandbox — and healthcare growth equity fund Blue Heron Capital.

Insightin Health plans to use the new funds to scale its proprietary inGAGE platform, which is designed to help payers retain and engage their members, said Enam Noor, CEO of Insightin Health, in an email.

The platform applies natural language processing to clinical information and social determinants of health and then generates an algorithm for care management and retention for each member, he explained.

“The AI-driven approach and machine learning algorithm connects the dots for all of the data sources and aggregates the trends for predictive outcomes,” he said. “[The platform makes] recommendations for every touchpoint of member communications and activities.”

Currently, the platform has aggregated data for more than 5 million people.

Scaling the inGAGE platform will allow Insightin Health to bring in new clients and provide new tools within the platform for existing customers.

“[Blue Venture Fund is] committed as an organization to improve the health system, and we feel a solution like Insightin Health takes great strides in getting us there,” said Binoy Bhansali, managing director of the Blue Venture Fund, in a news release. “Their ability to leverage data strategically elevates Medicare Advantage and Managed Medicaid plans’ abilities to improve quality, reduce costs, and drive growth and retention by better engaging members.”

Other investors in the funding round included Health Catalyst Capital, Revolution’s Rise of the Rest fund and SaaS Ventures.

The market for personalized consumer engagement platforms is crowded, with companies like Zipari, Welltok and Health Mine all jostling for market share. In addition, large enterprise companies are moving into the healthcare space, such as SalesForce and Microsoft, Noor said.

But Insightin Health sets itself apart by being the only single platform that is able to aggregate all data points for both structured and unstructured data into one ecosystem, he added.

In contrast to many companies that have suffered amid the Covid-19 pandemic, Insightin Health has fared well.

Though Noor declined to share the company’s current valuation, he said Insightin Health experienced 170% revenue growth year-over-year from 2019 to 2020. The company is aiming for similar growth this year.

The company’s growth during the pandemic is partly due to the fact that Insightin Health developed tools focused on the senior population in March 2020, Noor said. These tools gather data on Medicare and Medicaid beneficiaries and then use machine learning to help payers determine patient risk factors.

Photo: StockFinland, Getty Images

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UPMC Enterprises, the innovation and commercialization arm of Pittsburgh-based UPMC, launched Astrata. The company has developed natural language processing technologies that aim to improve the quality of care.

Astrata’s technologies help payers and providers assess care delivery in accordance with quality measures, like the National Committee for Quality Assurance’s Healthcare Effectiveness Data and Information Set (HEDIS). These measures aim to ensure the delivery of high-quality care and mitigate gaps.

Increasingly, the government and private payers are tying provider compensation to quality measures amid the move toward value-based care.

The company’s technologies can comb through hundreds of millions of clinical notes, and by applying natural language processing, can identify whether a patient has received care that is compliant with quality measures, said Dr. Rebecca Jacobson, president of Astrata, in a phone interview.

“[The platform] is really evaluating all the text across one member or one patient and [then it] says, yes this person is in the denominator, that is, the measure applies to them,” she explained.

Once the platform has determined whether a measure applies to a patient, it then assesses whether the care they received is compliant with that measure.

For example, there is a HEDIS measure that focuses on older women with bone fractures who have not yet received appropriate imaging and treatment. Astrata’s platform is able to analyze clinical notes and pinpoint those patients.

“That’s a true gap,” said Jacobson. “That is someone that the health plan and the provider want to know about because they want to intervene quickly so that they can prevent subsequent morbidity and mortality.”

Further, the platform allows healthcare organizations to conduct year-round monitoring and quality improvement efforts as opposed to the current system, where quality rates for many HEDIS measures can only be determined once a year via a manual process.

Astrata partnered with UPMC Health Plan to develop and validate its technologies. The platform has been used by UPMC and UPMC Health plan for several years.

“Over the last two years, UPMC Health Plan abstractors found they can work up to 38 times faster with the implementation of Astrata’s NLP-assisted tools,” said Diane Holder, president and CEO of UPMC Health Plan, in a news release. “This partnership facilitates a more rapid and accurate flow of thorough, meaningful data between our quality team and our providers.”

With the launch of the spinout, Astrata’s tools are now available in the U.S. market.

The company also plans to increase its workforce by 30% in 2021.

Photo: sdecoret, Getty Images

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