Free EMR Newsletter Want to receive the latest news on EMR, Meaningful Use, ARRA and Healthcare IT sent straight to your email? Join thousands of healthcare pros who subscribe to EMR and HIPAA for FREE!!

Health Data Standardization Project Proposes “One Record Per Person” Model

Posted on October 13, 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.

When we sit around the ol’ HIT campfire and swap interoperability stories, many of us have little to do but gripe.

Is FHIR going to solve all of our interoperability problems? Definitely not right away, and who knows if it ever will? Can we get the big EMR vendors to share and share alike? They’ll try, but there’s always a catch. And so on. There’s always a major catch involved.

I don’t know if the following offers a better story than any of the others, but at least it’s new one, or at least new to me. Folks, I’m talking about the Standard Health Record, an approach to health data sharing doesn’t fall precisely any of the other buckets I’m aware of.

SHR is based at The MITRE Corporation, which also hosts virtual patient generator Synthea. Rather than paraphrase, let’s let the MITRE people behind SHR tell you what they’re trying to accomplish:

The Standard Health Record (SHR) provides a high quality, computable source of patient information by establishing a single target for health data standardization… Enabled through open source technology, the SHR is designed by, and for, its users to support communication across homes and healthcare systems.

Generalities aside, what is an SHR? According to the project website, the SHR specification will contain all information critical to patient identification, emergency care and primary care along with background on social determinants of health. In the future, the group expects the SHR to support genomics, microbiomics and precision medicine.

Before we dismiss this as another me-too project, it’s worth giving the collaborative’s rationale a look:

The fundamental problem is that today’s health IT systems contain semantically incompatible information. Because of the great variety of the data models of EMR/EHR systems, transferring information from one health IT system to another frequently results in the distortion or loss of information, blocking of critical details, or introduction of erroneous data. This is unacceptable in healthcare.

The approach of the Standard Health Record (SHR) is to standardize the health record and health data itself, rather than focusing on exchange standards.

As a less-technical person, I’m not qualified to say whether this can be done in a way that will be widely accepted, but the idea certainly seems intuitive.

In any event, no one is suggesting that the SHR will change the world overnight. The project seems to be at the beginning stages, with collaborators currently prototyping health record specifications leveraging existing medical record models. (The current SHR spec can be found here.)

Still, I’d love for this to work, because it is at least a fairly straightforward idea. Creating a single source of health data truth seems like it might work.

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.

Incorporating More Social and Behavioral Data Into an EHR

Posted on June 11, 2015 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.

You all have seen the stats about how our social and behavioral data is a much bigger predictor of our health and wellness than the 15 minutes of data that’s collected during a doctors visit. When people talk about the out of control costs of US healthcare, they often point to these stats and talk about how we have to focus on factors outside of our current healthcare system if we really want to bend the cost curve.

If in fact we had a true healthcare system that was trying to treat the health of a patient and not the symptoms, then we’d take a much more serious look at the social and behavioral determinants of health. The shift in healthcare is to try and make this a reality and to shift the current reimbursement model to one that pays our healthcare organizations to keep the patient healthy and not just treat their chief complaint.

With this in mind, I was intrigued by this IOM report on Capturing Social and Behavioral Domains and Measures in Electronic Health Records. In the report (there are actually 2 phases of the report) they identify 17 areas that influence a patient’s health and wellness. Then, they narrowed it down to 11 domains to consider incorporating into all EHRs.

You can take a look at the report to find all the details of their findings. However, I found their list of 17 social and behavioral domains that influence your health and wellness absolutely fascinating. Here’s the list:

Sociodemographic Domains

  • Sexual orientation
  • Race and ethnicity
  • Country of origin/U.S. born or non-U.S. born
  • Education
  • Employment
  • Financial resource strain: Food and housing insecurity

Psychological Domains

  • Health literacy
  • Stress
  • Negative mood and affect: Depression and anxiety
  • Psychological assets: Conscientiousness, patient engagement/activation, optimism, and self-efficacy

Behavioral Domains

  • Dietary patterns
  • Physical activity
  • Tobacco use and exposure
  • Alcohol use

Individual-Level Social Relationships and Living Conditions Domains

  • Social connections and social isolation
  • Exposure to violence

Neighborhoods and Communities

  • Neighborhood and community compositional characteristics

As we start to see EHR vendors move from digital filing cabinets to actually keeping a population healthy, I’m going to be watching how they incorporate all of this social and behavioral health data into the EHR.

I think you could break out every one of these domain areas and create a company around collecting this data which could then be made to be consumable by an EHR vendor. In fact, if you look at the world of healthcare IT startups we already see a lot of companies that are working in these areas. The most obvious is the dietary patterns and physical activity domains. How many hundreds of healthcare IT startup companies are working on quantifying those areas of our lives? A wise entrepreneur might look at this list and find a less obvious area where they could improve people’s health.

My biggest takeaway from this list: Healthcare still has such an amazing opportunity to improve health. We’ve barely just begun to tap into this data.