The Time Is Now for Health Systems to Get Serious About Social Determinants of Health

A fundamental question continuing to face many health system executives is: How do we comprehensively address the needs of patients when those needs extend beyond the boundaries of traditional clinical care?  As President and CEO of PCCI, we have been focusing on this very challenge since 2012.  And while there has been much talk and excitement about social determinants of health (SDOH), we believe that ~90% of the health system market still does not leverage social/economic information when designing population health programs, developing patient-specific treatment plans, locating new services, or conducting community needs assessments. But before health system executives can design an effective SDOH strategy for their organizations, they must first assess where they are and where they would like to be based on the insights and advantages a progressive SDOH strategy would offer. PCCI’s Social Determinants of Health Maturity Model can help executives take this critical step.

Social Determinants of Health Maturity Model

Level Zero: Incomplete Picture of an Individual’s Environment

Realistically, this is the starting baseline for most organizations. Often, teams will attempt to use clinical and claims data ALONE as a means to segment patient populations and project the impact on a patient or cohort. This rarely works; rather, it often leads to late treatment in acute environments, sub-optimal interventions, and erroneous insights about specific patients, patient populations, or geographic markets.

Level 1: High-Level View of SDOH, Using Specific Social and Economic Indicators as Proxies

Teams can extract basic information from claims or clinical data that could serve as effective SDOH proxies. An example would be to look at the number of changes in addresses in a specific record, over a 12-month period, as a strong indicator of housing instability.  At the highest level, teasing out information from existing records can begin to illuminate some of the critical social and economic challenges that may present for individuals in a given community. This level of insight also allows health- system teams to test basic assumptions about a market. We’ve seen teams fooled when the employment level appears to be relatively stable, only to subsequently discover that much of the employment is via low-wage jobs with very poor benefits.  If you begin to see that people are moving around even though the employment statistic looks stable, you begin to realize that the actual stability of your community might not be what you perceived it to be.

If at Level 1, Leadership Teams Should Be:

  • Developing high-level proxy indicators to reflect underlying social and economic challenges that could play a significant role in health status or the ability to access services.
  • Understanding the payer mix; who you serve and, even within the insured population, understand the wage/income levels because there is a high percentage of employed, low-wage individuals that have vulnerabilities associated with transportation, housing, affordable daycare, etc.
  • Becoming familiar with existing local or state connected communities of care programs or activities aligning providers and community-based organizations, such as food pantries, to streamline assistance efforts, reduce repeat crises and emergency funding requests, help address disparities of care, and improve the health, safety, and well-being of residents.

Level 2: Root Causes Understanding of Poor Outcomes at the Population Level

The rubber hits the road at level 2 and teams begin leveraging local data that directly reflect variation in social determinants. We believe that to understand root causes and build actionable models for patient engagement and support, you must evaluate data at the block level. Zip-code level aggregation often masks important details. This is particularly true in highly populated municipalities that can see a tremendous amount of social determinant variation within a 0.1 mile distance. For example, if I had block-level information providing insight that a six-block neighborhood within my market was having transportation-oriented issues and concentrated pockets of non-violent crime, I would model these insights into the deployment of my mobile diagnostic clinics or my development of innovative models to improve access.  Also, if I was discharging a patient who resided in that neighborhood, I would rethink how to schedule follow-up appointments, since the chances of the patient keeping the visits are extremely low. This level of insight and actionability would be missed at the zip-code level.

In collaboration with DFWHC Foundation, Community Council of Greater Dallas, and the University of Texas at Dallas, PCCI built a platform for Dallas called Dallas Community Data for Action and/or Community Data Insights [CDI].  CDI ingests and organizes multiple, publicly available data inputs, such as housing, education, food availability, and 911 and 311 data to generate real-time, actionable dashboards containing over 60 factors that all point to specific social determinants.  In Dallas, use of this data has been vital in understanding pockets of need and in locating areas where the impact of interventions can be the most profound.  You can also use this data more broadly to generate support to build community cross-sector collaboration, by enabling health systems to effectively  engage and coordinate with local municipality officials on community-based support services and planning, and also by helping philanthropic organizations to better understand (and track) community needs in order to invest in/prioritize funding areas that will produce the greatest impact.  In addition to having a detailed and dynamic picture of social and economic needs (demand for services), the CDI dashboard can quickly map out where support services are available/delivered and map/model the interdependencies and concentration of chronic health conditions with social support needs.  As this model is rapidly scalable, PCCI is already working with others across the country.

If at Level 2, Leadership Teams Should Be:

  • Integrating SDOH market insights into your strategic planning process and your community engagement plan
    • Use block-level SDOH in community needs assessments
  • Anticipating and predicting the correlations between multiple social and economic factors to inform your patient flow and access strategy (including your telehealth strategy). Start conducting trend analyses to anticipate and forecast the changes in local-market dynamics that will impact utilization, payer mix, and social/economic barriers to health.
  • Crafting a data-driven engagement plan to align more directly with local municipalities and local philanthropic organizations.

Level 3: Comprehensive Partnership Between a Community’s Clinical and Social Sectors

Participating organizations across a community are collaborating on one Information Exchange Platform and are connected through an innovative closed-loop referral system allowing them to communicate and share information with each other. Success at this highest level requires both a strong technology infrastructure and consistent programmatic deployment [at scale] across a community. This is what we’ve done in Dallas with our technology partners at Pieces Technology Inc.; effectively managing the right balance of people, processes, and technology has allowed us to achieve the positive results that we’ve seen.

Level 3 means a significant investment and a multi-year commitment, not only by the anchoring healthcare system or systems, but also by the local community.  It requires an initial investment and a robust sustainability plan that can ensure that the platform capabilities evolve with the changing needs of the community.  Deployment requires not only new technology, but an engaged local governance structure, new legal and data sharing agreements, and further refinement of data integration and advanced analytics at the individual level.  Integrating these into new/updated clinical and community workflows enables teams to proactively predict specific health and social/economic needs, the complexity and co-dependency of needs, and the ability to act real time at the point of care to address these needs.  This can facilitate making real-time referrals for community support services, tracking whether individuals accessed suggested medical or community resources (and what specific services were provided), and measuring and tracking the impact to individual/community resiliency, self-sustainability, health outcomes, and cost.  In Dallas, we’ve also started to leverage advanced data algorithms to risk-stratify individuals based on their health and social/economic needs to better prioritize and tailor resources and to proactively target high-risk individuals for engagement and follow-up via digital technology.

At levels 2 and 3, a health system must also think about how to leverage its foundation resources and internal employee community-outreach volunteer programs.  Once you better understand the patients that you’re serving in your market and the community-based services they access, you can better deploy employee-based efforts and philanthropic activities that align with the strategic efforts and provide maximal impact.

If at Level 3, Leadership Teams Should Be:

  • Crafting the information exchange platform governance infrastructure to delineate key roles, essential participants, and shared objectives.
  • Committing to cross-community collaboration [potentially including competitors] and a long-term effort; recognizing that your health system might be an anchor organization, but it cannot independently solve the entire problem.
  • Selecting and deploying the technology infrastructure [Pieces Iris™, TAVHealth, Unite Us, etc.] to enable cross-community engagement.  Develop updated clinical and community-based workflows.

In summary, if you’re just starting to address SDOH, you’re late.  It is critical for health systems to begin their SDOH journey today, especially if you serve a vulnerable population and/or operate in a market dominated by uninsured and Medicaid patients.  Addressing SDOH is also equally important for organizations managing a lower-wage, commercially insured population and for any health system that is actively managing or considering taking on risk-based contracts.

If you’re well on your way up the SDOH curve and actively integrating SDOH into your strategic and care-delivery models, then start working on new models to bridge social isolation (physical and mental) and to better understand (and develop strategies to address) challenging behaviors, including chronic helplessness.

To learn more about our Dallas journey, please visit our website and see what our team of PCCI experts is doing to make a difference or visit our technology partners at Pieces Technology to experience the Pieces IRIS™ technology.

My Summer as a Data Science Intern at PCCI

For the short duration of returning to my hometown Dallas for the summer, I’ve been interning at Parkland Center for Clinical Innovation (PCCI) as a Data Science Intern. During my interview with Albert and Vikas, we discussed some issues with the representation of data in the current healthcare system. Hospitals use different coding systems in their electronic medical records (EMRs), making communication between hospitals and care providers difficult. A while ago, a new health data standard called FHIR (Fast Healthcare Interoperability Resource, pronounced “fire”) was proposed. My project this summer aimed at identifying whether data could be easily transformed into the new FHIR format, carrying out the transformation, and creating predictive models using the new FHIR data.

Situated on the 11th floor of the building, PCCI is a very chill place to work. Quiet spaces are easily found at desks and conference rooms scattered around the office. As an intern, I sit on the “Intern Island” with (usually) 6 other interns. I like this space because we get two monitors and a Lenovo Thinkpad.

 

Emily Wang, PCCI Data Science Intern
Emily Wang, PCCI Data Science Intern

As for work, each PCCI project usually consists of one project manager, a clinical expert, and a data scientist. The intern projects are no different; Aaron was the FHIR Project Manager Intern, and Mila was the FHIR Clinical Intern. Both had important but separate duties that helped our project succeed.

As the Data Science Intern on the FHIR project, I was responsible for first converting the data into FHIR resources.  This involved bringing back Java knowledge from several years ago! There were definitely some issues figuring out how to add the right dependencies because Java can get complicated very quickly. A few days were spent just trying to get oriented with Java and Eclipse, and making sure all the necessary packages for FHIR were installed.

We were working with two years of data. This roughly translates into 27 million (!) vitals and 17 million labs, and each vital and lab was converted into its own separate file. I quickly realized that there would be no space on my laptop to hold all of these files, so we decided to enlist the help of Microsoft Azure. With Azure, the task became less difficult, but still, the hardest part of my summer was working with such huge numbers of files.

Caught up in the huge task of transforming vast amounts of data to FHIR resources, I left very little time in my internship to work on actual data science. Out of the approximately 13 weeks total, about six weeks were spent converting the table format EMR data into FHIR resources, five weeks were spent on parsing the FHIR resources into a format for machine learning, and the remaining two weeks were dedicated to model building. Reflecting back, I would definitely work harder to cut short the resource conversion in favor of more time for data science.

 

"As an intern, I sit on the “Intern Island” with (usually) 6 other interns. I like this space because we get two monitors and a Lenovo Thinkpad." said Emily Wang
“As an intern, I sit on the “Intern Island” with (usually) 6 other interns. I like this space because we get two monitors and a Lenovo Thinkpad.” – Emily Wang

As a Data Science Intern at PCCI, you have the freedom to work in any language you want; the full-time Data Science team is very evenly divided between R and Python. There’s also a lot of freedom in dictating which path your project will go. Your supervisor will point you in a very general direction of where to go and state goals and expectations, but is otherwise very lenient!

Don’t be shy about asking around people for advice and help, even if they’re not on your project team! Even though most people are busy with various meetings, they will gladly schedule a 30-minute or even hour-long block to discuss your project privately with you.

When presenting your project, whether it’s a progress update or final presentation, expect multiple questions from the audience. It’s not that they want to quiz you on your knowledge and preparation on your project, but because they’re genuinely curious and care about understanding what you’re doing over the summer.

A mandatory 30-minute lunch is required every day. I recommended bringing lunches that can stay in the fridge for several days (like salad) or not bringing anything because there are often team lunches and random outings during the day. Occasionally there’s leftover pizza or sandwiches from lunch meetings in the big conference room or leftover burritos from breakfast.

I enjoy the diverse atmosphere at PCCI the most. The three teams: Data Science, Project Management, and Clinical teams collaborate and work together so well. It’s a very fluid system. A data scientist with a question about the best intervention methods for patients with diabetes can easily walk over to a clinical team member and get an answer within minutes. Despite being employed as a data scientist, you have access to an entire host of medical knowledge from the clinical team and connections from the project management team.

My biggest takeaway from this internship is learning about long-term time management and collaboration. Manage your time well and you’ll be able to at least touch on everything you wanted to learn during your internship. Collaborate with as many people as you can, so not only can you learn so much more but also gain friends and connections while doing so.

Hired at “I wrote you a code”

First Question, Lasting Impression

How often did you get the question (or have asked it yourself) during an interview: “Why are you interested in our organization?” Simple, standard, mundane – on the surface, some might even call the question un-inspiring. I disagree. It tells me right off the bat how much effort the candidate put into learning about the Parkland Center for Clinical Innovation, our organization, our work, our team and how they synthesized and interpreted the information. I can tell within the first two minutes how interested I will be for the next 28. Regardless of the level of experience, I’m way too often disappointed by the response.

Using Python to Create a Sentiment Analysis

Recently, I was blown away! I asked the same “boring” question to a candidate interviewing for an entry data science position at PCCI. As soon as I finished the question, his eyes lit up and he quickly pulled out a document from his bag. With great enthusiasm, he replied:

Python, PCCI, PCCI Word Cloud

“In addition to my own research, I wanted to know what others are saying and feeling about PCCI. So, I wrote code in Python to create a Twitter sentiment analysis. I used it to create a word cloud and analyzed it to see if the keywords match my passion and interpretation of my own research. These four words really resonated with me because … I also wanted to understand PCCI’s reach and brand recognition, so I analyzed the top 10 famous people and companies talking about PCCI. I was impressed to see @HarvardBiz, @washingtonpost, @NIH, @HHSGov, etc, but most importantly to see @KirkDBorne. He’s so influential. Finally, the outputs of the Sentiment Count Plot Analysis and the Sentiment Subjectivity Distribution reconfirmed that this is a great place and the place I want to be.”

A Match Made in Data Science

I know I’m a geek at heart and this answer resonated with me more than it would with most (did I mention earlier how important it is to know your audience and their interests when answering a question?), but regardless of what approach you take, this is how you do it!

Stay up-to-date with PCCI’s data science work by checking our recent news and follow us on Facebook, Twitter and LinkedIn!

 

PCCI’s Innovation Bridge Opens at Parkland’s Hatcher Station Health Center

Enabling patients to connect to digital technology to better manage their own health

Dallas, TX –The Parkland Center for Clinical Innovation (PCCI) in collaboration with Parkland Health & Hospital System has deployed the Innovation Bridge at the Hatcher Station Health Center. Serving residents of East Dallas, Hatcher Station is one of 12 Community Oriented Primary Care centers (COPCs) operated by Parkland throughout Dallas County. Taking inspiration from the Apple Genius Bar and Ochsner Health System’s O Bar, the Innovation Bridge is designed to bridge the technology divide for patients and caregivers.

Although there are a number of mobile apps in the marketplace that claim to help patients manage a variety of different health conditions, many are not vetted by clinicians or targeted for vulnerable patients. PCCI’s Innovation Bridge was developed by Parkland and PCCI physicians in collaboration with IT experts.

“We are very excited to launch and test the impact of this innovative concept,” said Steve Miff, PhD, President and CEO, Parkland Center for Clinical Innovation. “New technology can be intimidating, so we aim to use a physical space to bridge the technology adoption gap. Linking individuals to relevant digital information is a key step towards personal activation and participation in self-management of health. We are very appreciative for the support and partnership of all our funding partners and our clinical colleagues who helped make this initiative possible.”

(Left to right) James Perez, Dr. Fred Cerise, Steve Miff, Dr. Esmaeil Porsa, Paula Olson, Jessica Hernandez, Stephanie Fenniri, Gretchen Collins, and Vikas Chowdhry at the opening of the PCCI’s Innovation Bridge at Parkland’s Hatcher Station Health Center.

For those not familiar or comfortable with mobile technology available to them, the Innovation Bridge will be staffed by a bilingual Community Technology Liaison who will assist in selection and set up of the right app for each patient’s needs. The person will help guide patients to digital technologies such as MyChart that improve patient engagement, real-time access to personal clinical information, and connections to relevant community resources. An initial list of apps in the following categories has been vetted by PCCI’s team of experts: Asthma, Diabetes, Hypertension, Women’s Health (Pregnancy and Breastfeeding), Mental Health, Pediatric Milestones, Obesity Management, Fitness Tracking and Back Exercises.

“Technology continues to be an essential partner in innovative care delivery at Parkland. As the demand for our services escalates, we are focused on providing the highest possible quality of care to each patient. Innovations like mobile apps empower patients to be full partners with their physicians to assure they receive appropriate medical care and we are pleased to offer the PCCI Innovation Bridge to encourage patients to take advantage of all the resources available to them,” said Fred Cerise, MD, MPH, Parkland’s CEO.

The PCCI Innovation Bridge is located at Parkland’s Hatcher Station Clinic, 4600 Scyene Road, Dallas, 75210. For more information about Parkland, visit www.parklandhospital.com

About PCCI

PCCI is an advanced, nonprofit healthcare analytics R&D organization with a collaborative team of expert data scientists and knowledgeable healthcare professionals that go beyond analyzing a patient’s medical data to provide all-encompassing insights that are revolutionizing healthcare. PCCI is a recipient of more than $50 million in grants directed at developing and deploying patient centric cutting edge technologies connecting communities, Parkland and beyond.

Contact

PCCI
Lindsey Nace, Marketing and Communications
214-590-3887 lindsey.nace@PCCInnovation.org

Parkland Center for Clinical Innovation (PCCI) Announces New Branding

Updated branding highlights strategic direction; expands sharing of PCCI’s research and innovation

Dallas, TX — PCCI, a nonprofit healthcare advanced analytics research and development organization, is pioneering new ways to health. Starting last year with the hiring of their President and CEO, Steve Miff, PhD, PCCI has expanded its teams. The company’s leadership team now includes Aida Kreho as Vice President of Operations, Vikas Chowdhry as Vice President of Data Strategy and Analytics, and Keith Kosel as Vice President of Enterprise Relationships. They join a growing team of leading clinicians and nationally recognized data scientists. PCCI also updated its strategic direction, and expanded their partnerships in DFW and across the country. The innovation projects resulting from these collaborations are increasingly being highlighted in prominent national publications and conferences. New branding and website designs complement and support these efforts.

“The new PCCI messaging and website are designed to make our work more personable and approachable, and to streamline our ability to collaborate and communicate,” said Steve Miff. “This process involves enhancing our ability to share the models we introduce and the knowledge we generate through our innovation and co-creation processes.”

The PCCI colors have been updated, and the logo has been completely redesigned with elements of both innovation and data, with a slight nod to the original tree. The new website (PCCInnovation.org) is centered around the people, stories, and communities that PCCI serves. PCCI’s bold journey that started with one Parkland patient, continues to aspire to develop new and innovative solutions to deliver individualized, precision health aligned with social care.

Steve Miff, PhD added, “I am excited to not only continue our great progress, but aim to expand our impact to ‘pioneer new ways to health.’ We have a unique opportunity to leverage our expert data scientists and knowledgeable healthcare professionals. Leveraging our partnerships enables us to create connected communities that align resources and drive precision, personalized interventions, and engage individuals in their own health.”

PCCI’s mission to reimagine and expand the knowledge base of healthcare through prescriptive analytics and artificial intelligence, remains the focus. The new branding and website further facilitates the company’s capacity to share their data science, artificial intelligence and predictive model expertise, and bring resources to their partners and the communities they serve.

About PCCI
PCCI is an advanced, nonprofit healthcare analytics R&D organization with a collaborative team of expert data scientists and knowledgeable healthcare professionals that go beyond analyzing a patient’s medical data to provide all-encompassing insights that are revolutionizing healthcare. PCCI is a recipient of more than $50 million in grants directed at developing and deploying patient centric cutting edge technologies connecting communities, Parkland and beyond.

Contact
Lindsey Nace, Marketing and Communications
214-590-3887
lindsey.nace@PCCInnovation.org