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Translating Social Determinants of Health Into Clinical Action

Posted on September 25, 2017 I Written By

The following is a guest blog post by Anton Berisha, MD, Senior Director, Clinical Analytics and Innovation, Health Care, LexisNexis Risk Solutions.
The medical community recognizes the importance of social determinants of health (SDOH) – social, economic and environmental conditions in which people are born, grow, live, work and age that impact their health – as significant and direct risk factors for a large number of health care outcomes.

The negative outcomes include stress, mental health and behavioral disorders, alcoholism and substance abuse, to name a few. Negative SDOH worsen a slew of major chronic conditions, from hypertension and Coronary Artery Disease to obesity; they also lead to lower patient engagement and medication adherence while increasing low-intensity ER visits and hospital admissions and readmissions.

In fact, a study shows that medical care determines only 20% of overall health outcomes while social, economic and environmental factors determine about 50% of overall health. The National Quality Forum, Centers for Disease Control and Prevention and World Health Organization have all acknowledged the importance of addressing SDOH in health care.

Not all SDOH are “created equal”

When it comes to SDOH, there is a misconception that all data regarding a person’s lifestyle, environment, situation and behaviors relate to their health. Although there is a myriad of basic demographic data, survey data and other Electronic Health Records (EHR) data available to providers today, much of it has a limited potential for identifying additional health costs and risks.

The key to addressing SDOH is to use current, comprehensive and longitudinal data that can be consistently linked to specific patient populations and provided in a standardized format. One example is attributes derived from public records data such as proximity to relatives, education, income, bankruptcy, addresses and criminal convictions.

Moreover, each SDOH attribute has to be clinically validated against actual healthcare outcomes. Clinically validating attributes is critical to successful predictive analytics because some attributes do not correlate strongly to health outcomes.

For example, while knowing how close an individual’s nearest relative or associate lives to the patient does correlate to health outcomes; knowing how many of those relatives or associates have registered automobiles does not. Even when attributes are clinically validated, different attributes correlate to different outcomes with different accuracy strengths.

Translating SDOH into actionable intelligence

After SDOH have been correlated to healthcare outcomes, providers have two implementation options. One is to use relevant individual SDOH attributes per outcome in clinical and analytic models to better assess and predict risk for patients. Another is to use SDOH as part of risk scores estimating specific healthcare risks; for e.g., to estimate an individual’s total health care risk over the next 12 months based on cost; a 30-day readmission risk; or a patient engagement score.

Risk estimation can be done either in combination with other types of legacy healthcare data, such as claims, prescription and EHR data or with SDOH alone, in the absence of medical claims.

Recently, a client of LexisNexis® Health Care did an independent study to evaluate the impact and usefulness of Socioeconomic Health Score (SEHS) in risk assessment for several key chronic conditions, when no other data are available. Findings proved that the top decile of SEHS captures significantly more members with given conditions than the bottom decile. The study concluded that the difference was important and very helpful in estimating risks in a newly acquired population without legacy healthcare data.

Integrating SDOH into clinical workflows and care recommendations

Validated SDOH can be presented in a form of risk drivers or reason codes directing the clinician toward the most important factors influencing a given negative outcome for each patient: income, education, housing or criminal records.

The risk drivers and reason codes can then be integrated into workflows within the clinician’s IT systems, such as the EHR or care and case management, in the form of an easy-to-understand presentation. It could be a data alert that is customizable to patients, treatments and conditions, helping the provider make score-based decisions with greater accuracy and confidence. At this point, the SDOH information becomes actionable because it has the following characteristics:

  • It is based on hard facts on every individual.
  • It is based on correlation and statistical significance testing of large pools of patients with similar behavior.
  • It provides clear and understandable reason codes driving the negative outcomes.
  • It can be tied to intervention strategies (outlined below) that have demonstrated positive results.

Clinicians empowered with actionable SDOH information can modify their interventions and follow-up strategies accordingly. Based on resources at hand, patients living in negative SDOH could be either properly managed by clinicians themselves or other medical staff, social workers and newly created roles such as health coaches. Sub-populations at risk could benefit from access to community resources to get help with housing (permanent supportive housing for homeless), transportation, education, childcare and employment assistance.

Moreover, SDOH are particularly effective in helping providers develop a population health management strategy fueled by prioritized tactics for preventive care. Tactics can range from promotion of healthy food to free screening services. For patients with chronic diseases (who can typically be managed appropriately when they adhere to therapy and healthy lifestyle choices), SDOH-informed interventions can help keep them under control and potentially reduce severity. For patients recently released from the hospital, aftercare counseling could prevent complications and readmissions.

To sum it up

Socioeconomic data is a vital force for healthcare risk prediction as it provides a view into the otherwise hidden risks that cannot be identified through traditional data sources. When SDOH are clinically validated and correlated to healthcare outcomes, they help providers better understand an individual’s risk level and address it through appropriate intervention strategies.

Searching EMR For Risk-Related Words Can Improve Care Coordination

Posted on September 18, 2017 I Written By

Anne Zieger is a healthcare journalist who has written about the industry for 30 years. Her work has appeared in all of the leading healthcare industry publications, and she’s served as editor in chief of several healthcare B2B sites.

Though healthcare organizations are working on the problem, they’re still not as good at care coordination as they should be. It’s already an issue and will only get worse under value-based care schemes, in which the ability to coordinate care effectively could be a critical issue for providers.

Admittedly, there’s no easy way to solve care coordination problems, but new research suggests that basic health IT tools might be able to help. The researchers found that digging out important words from EMRs can help providers target patients needing extra care management and coordination.

The article, which appears in JMIR Medical Informatics, notes that most care coordination programs have a blind spot when it comes to identifying cases demanding extra coordination. “Care coordination programs have traditionally focused on medically complex patients, identifying patients that qualify by analyzing formatted clinical data and claims data,” the authors wrote. “However, not all clinically relevant data reside in claims and formatted data.”

For example, they say, relying on formatted records may cause providers to miss psychosocial risk factors such as social determinants of health, mental health disorder, and substance abuse disorders. “[This data is] less amenable to rapid and systematic data analyses, as these data are often not collected or stored as formatted data,” the authors note.

To address this issue, the researchers set out to identify psychosocial risk factors buried within a patient’s EHR using word recognition software. They used a tool known as the Queriable Patient Inference Dossier (QPID) to scan EHRs for terms describing high-risk conditions in patients already in care coordination programs.

After going through the review process, the researchers found 22 EHR-available search terms related to psychosocial high-risk status. When they were able to find nine or more of these terms in the patient’s EHR, it predicted that a patient would meet criteria for participation in a care coordination program. Presumably, this approach allowed care managers and clinicians to find patients who hadn’t been identified by existing care coordination outreach efforts.

I think this article is valuable, as it outlines a way to improve care coordination programs without leaping over tall buildings. Obviously, we’re going to see a lot more emphasis on harvesting information from structured data, tools like artificial intelligence, and natural language processing. That makes sense. After all, these technologies allow healthcare organizations to enjoy both the clear organization of structured data and analytical options available when examining pure data sets. You can have your cake and eat it too.

Obviously, we’re going to see a lot more emphasis on harvesting information from structured data, tools like artificial intelligence and natural language processing. That makes sense. After all, these technologies allow healthcare organizations to enjoy both the clear organization of structured data and analytical options available when examining pure data sets. You can have your cake and eat it too.

Still, it’s good to know that you can get meaningful information from EHRs using a comparatively simple tool. In this case, parsing patient medical records for a couple dozen keywords helped the authors find patients that might have otherwise been missed. This can only be good news.

Yes, there’s no doubt we’ll keep on pushing the limits of predictive analytics, healthcare AI, machine learning and other techniques for taming wild databases. In the meantime, it’s good to know that we can make incremental progress in improving care using simpler tools.

We Don’t Use the Context We Have in Healthcare

Posted on June 1, 2017 I Written By

John Lynn is the Founder of the HealthcareScene.com blog network which currently consists of 10 blogs containing over 8000 articles with John having written over 4000 of the articles himself. These EMR and Healthcare IT related articles have been viewed over 16 million times. John also manages Healthcare IT Central and Healthcare IT Today, the leading career Health IT job board and blog. John is co-founder of InfluentialNetworks.com and Physia.com. John is highly involved in social media, and in addition to his blogs can also be found on Twitter: @techguy and @ehrandhit and LinkedIn.

I was recently looking at all the ways consumer technology has been using the context of our lives to make things better. Some obvious examples are things like Netflix which knows what shows we watch and recommends other shows that we might enjoy. Amazon knows what we’ve bought before and what we’re searching for and can use those contexts to recommend other things that we might want to consider. I know I’ve used that feature a lot to evaluate which item was the best for me to purchase on Amazon.

Everywhere we turn in our consumer lives, our context is being used to provide a better experience. Sometimes this shows up in creepy ways like the time a certain cleaning product was mentioned in my kitchen and then I saw an ad for it on a website I was visiting. Was it just coincidence or did Alexa hear me talking about it and then make the recommendation to buy based on that data? Yes, some of this stuff can bit a little creepy and even concerning. However, I personally love the era of personalization which generally makes our lives better.

While this is happening everywhere in our personal lives, healthcare has been slow to adopt similar technologies. Far too often we’re treated in healthcare without taking into account the context of our needs. Sometimes this is as simple as a healthcare provider not taking time to look at the chart. Other times we deny patients request that we add their medical record to our own record or we store it in a place where no one will ever actually access it.

Those are just the basic ways we don’t use context to help us better serve patients. More advanced ways are when we deny patients the opportunity to share their patient generated health data or we don’t use the health data they’re providing. Many people are working on pushing out social data which can provide a lot of context into why a patient is experience health issues or how we could better treat them. This is only going to grow larger, but we’re doing a poor job finding ways to seamlessly incorporate this data into the care that’s being provided.

One of the big challenges of AI is that it has a hard time understanding context. However, humans have a unique ability to include context in the decisions they make. Our interfaces should take this into account so that humans have the information they need to be able to make the proper contextual decisions. At least until the robots get smart enough to do it themselves.

Have you seen other places where healthcare didn’t use the context of the situation and should have used it? How about examples where we use context very effectively?