Cerner Health Conference 2018: Interest in PHM Solutions Remain ‘Healthe’
By Matt Guldin and John Moore
Recently, we and 12,000+ others attended the Cerner Health Conference 2018, where the theme was “Smarter Care.” Overall, the event focused on building on top of the EHR, while much of the floor space and conversations focused on population health and revenue cycle.
For several years now, Cerner has been focusing on moving beyond the EHR with its HealtheIntent PHM platform. At CHC18, Cerner was doubling down on this bet with the message “Smarter Care” with numerous sessions and a significant amount of exhibit space dedicated to this theme and platform. Today, Cerner has signed ~160 clients of which ~90 are currently live.
HealtheIntent is well positioned as an EHR agnostic solution that will give Cerner the ability to invest resources into developing solutions that think beyond the hyper-competitive zero-sum game of EHR contracts. Two major flagship HealtheIntent customers are also two major Epic customers (Advocate and Geisinger) and there is a clear opportunity to work with these customers to better integrate Cerner’s evolving platform with Epic’s similarly expanding universe of products.
A key challenge for any PHM vendor is developing strong services capabilities to assist clients in extracting the highest value from their PHM solution deployments. In August, Cerner made a significant investment in Lumeris, a company with a strong services offering that Cerner will leverage.
In line with the company’s shift toward consumer-focused healthcare, Cerner is partnering with Salesforce to offer providers an integrated patient engagement solution. Through this partnership, HealtheIntent data, which is collected from numerous sources, will feed directly into Salesforce’s Health Cloud.
Once the data is in Health Cloud, from within their EHR a clinician can quickly identify patient populations based on various criteria via a queried search. Once identified, a campaign can be initiated. Cerner plans to launch this product in 2019, and will be the only EHR company to have Salesforce directly embedded in its EHR and HealtheIntent workflows.
Cerner understands the importance of getting revenue cycle (billing) software right. Cerner has penetrated, to varying degrees, about 40% of its hospital clients with billing software, but those are mostly small facilities. Cerner’s software still appears visually outdated and lacking functionality, particularly for ambulatory practices.
One need only walk through the CHC18 exhibit area of third-party software vendors to see the demand for RCM solutions that work in the Cerner environment. Roughly 40 percent of all vendors were RCM vendors. Clearly, Cerner is missing out on fully capitalizing on this opportunity.
Cerner is seeing strong demand for its RevWorks offering among smaller hospitals, and the firm is still hiring aggressively to support growth into 2019. Cerner has about 100 RevWorks clients, compared to 30-35 two years ago. One of the main reasons for Cerner’s early success in outsourced revenue cycle solutions is that the product comes with a demonstrable return on investment for clients with specific targets outlined before any deal is signed.
The ITWorks business is growing but acquiring clients at a slower pace than RevWorks.
Now that MHS Genesis is up and running across the first wave of an initial 4 sites, the company took important lessons learned for future Department of Defense (DoD) rollouts as well as Veteran Affairs (VA) deployments. Cerner noted that the VA is not yet in the implementation phase as it is currently planning the largest install in the industry’s history.
Of these two massive installs, the one at the VA bears watching closely. A significant portion of veterans receive their care via Tricare (local healthcare providers under contract to VA). How Cerner drives interoperability across multiple venues of care nationwide, the potential role of HealtheIntent, the embedding of telehealth functionality and the list goes on will all be pressure-tested by the VA. The results of that pressure-testing will, in time, roll out to the broader Cerner client base.
“Where the company has truly led the EHR market is with HealtheIntent. Rather than a walled-garden approach, Cerner’s HealtheIntent is architected for a more open future and its capabilities continue to expand even though market has been tepid. Cerner accurately saw the future and invested early for the inevitable move to value based care.”
Cerner has been an innovative company since its founding. While not all innovations have been a success (much to some clients’ chagrin), the company has nonetheless made progress and continues to push forward. Their ambulatory EHR is gaining significant traction with larger IDN clients, and RCM—while not there yet—is closing the gap with competing solutions.
Where the company has truly led the EHR market is with HealtheIntent. Rather than a walled-garden approach, Cerner’s HealtheIntent is architected for a more open future and its capabilities continue to expand even though the market has been tepid. Cerner accurately saw the future and invested early for the inevitable move to value-based care.
Visionary leadership made Cerner what it is today. Hopefully, the company’s new leadership fully appreciates this key attribute. Only time will tell if the future focus of Cerner is operational efficiency at the expense of vision. While operational improvements are common in a maturing market, our hope is that Cerner continues to look beyond the near term.
Matt Guldin · 2 years ago
Liz Gavriel · 4 years ago
John Moore · 2 months ago
Matt Guldin · 2 months ago
Hannah Ehnle · 2 months ago
Marshalling aggregated EHR and claims data for use in applications is an ongoing challenge for most healthcare enterprises. Social determinants of health (SDoH) are a relatively new and amorphous data type that show great promise for contributing to a range of applications.
As healthcare shifts from volume to value, SDoH present opportunities at both population and individual levels. Patient cohort discovery in PHM programs could become more precise and accurate using relevant SDoH. SDoH offers the potential to “better predict potential healthcare outcomes across disparate populations.”
Implementers will need more experience with [SDoH] data before it becomes a routine inclusion in HIT applications. Otherwise, SDoH risk becoming just another unruly data source.
Providers and payers hope to achieve a better understanding of risk, better patient engagement, and more effective use of existing treatment resources. Questions remain about what qualifies as a SDoH, where to source such data, and how to use it. More experience will be needed before SDoH delivers broad-based benefits at both a patient and population level.
The CDC defined SDoH as “conditions in the environments in which people are born, live, learn, work, play, worship, and age that affect a wide range of health, functioning, and quality-of-life outcomes and risks.” While most organizations generally agree with this definition, different organizations report drastically different measures as important to health status.
A quick look at existing lists of SDoH confirms that there is no consistent, widespread acceptance for a single set of factors. For instance, the Kaiser Family Foundation reports 48 social determinants related to individual health, ranging from years of schooling to access to a full-service grocery store. Meanwhile, LexisNexis offers 442 measures that relate to patient health.
No governmental or commercial authority has established a definitive list with strong industry support. The CDC, Canadian government, and WHO have all produced reports outlining their take on which SDoH should be tracked, accompanied simply by policy and practice recommendations. For now, providers and payers are bombarded with different views of the sources, uses, and value of SDoH. Agreement on which SDoH are important would help implementers understand how and where to use this relatively new data type.
Despite uncertainty about which SDoH are relevant, many stakeholders believe that SDoH can help improve population and patient health. However, while most people accept that there is a correlation between SDoH and health outcomes, determining causality between specific kinds of SDoH and specific health outcomes remains challenging.
The correlation between an individual’s years of schooling completed and their probability of smoking provides a perfect example. While more years of education correlate to a lower probability of smoking, more education does not cause anyone to smoke less, and smoking does not cause anyone to drop out of school. Research shows that the differences in smoking behavior at age 24 are accounted for by differences in smoking behavior at age 17, implying that some third factor drives both the probability of smoking and the years of completed education. As a result, interventions intended to decrease the probability of smoking by increasing the years of education an individual completes would be ineffective.
While providers accept that SDoH are often correlated to health outcomes, lack of knowledge about causality hampers efforts to translate those correlations into effective interventions. In addition, SDoH data is not guaranteed to contribute in all contexts. A 2017 study showed that using SDoH does not enhance predictions about a patient’s need for social services beyond what EHR and claims data already provide. Implementers could use guidance on whether certain kinds of SDoH enhance an application.
SDoH is connecting healthcare stakeholders with organizations not previously thought of as directly involved in healthcare delivery. For example, after determining that better access to fresh produce, stable housing, and preventive screenings improves patients’ health, UnitedHealthcare awarded $1.95 million to organizations that could help. One recipient, Feeding Wisconsin, used the funds to expand support for local food banks.
Both the U.S. and Canadian governments have initiatives that mirror what UnitedHealthcare has done, but on a national level. Canada’s budget document, Growing The Middle Class, “detailed an unprecedented investment of $8.4 billion over five years in housing, education and child welfare for Indigenous peoples, and $2 billion to end longstanding boil-water advisories on reserves,” specifically citing SDoH as a reason for the investment. The U.S. initiative, Healthy People 2020, seeks to address SDoH by promoting economic stability, education, social and community context, health access and education, and built environment through funding provided by the Office of Disease Prevention and Health Promotion. These large initiatives fund smaller groups and programs that already have traction in addressing SDoH.
Sourcing relevant SDoH data requires payers and providers to engage in a different process from the collection of traditional health data. In recent years, data brokers such as LexisNexis, Experian, and Axciom have been controversially selling SDoH data derived from a variety of consumer data sources. These companies collect vast amounts of data and create patient “health scores” similar to credit scores. Using these health scores, payers and providers can identify at-risk populations and prescribe personalized treatment options for individual patients.
Consumer understanding of the existence of this data remains low. SDoH data has not historically been considered directly pertinent to healthcare and is not subject to HIPAA. The recent usage could trigger more market or regulatory scrutiny of SDoH.
The number and variety of organizations offering social determinant data is increasing. The social media giants have an interest in further monetizing the data they have collected. Absent evidence that such data helps providers to make more informed decisions about patient health, market acceptance is not assured.
Ultimately, the interest level and desire to leverage SDoH in health IT is increasing rapidly. HIT vendors are responding slowly by including this data type in different products, but still in narrowly defined ways. Wider availability of a variety of SDoH is also fueling interest and experimentation. Incorporating SDoH into existing stores of EHR and claims data at the patient or cohort level introduces another layer of complexity for developers. Implementers will need more experience with this data before it becomes a routine inclusion in HIT applications. Otherwise, SDoH risk becoming just another unruly data source.