12 June 2019

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.

14 November 2018

World Diabetes Day




Today, November 14, is designated as World Diabetes Day to unite the global diabetes community to produce a powerful voice for diabetes awareness and advocacy. According to the World Health Organization (WHO), over 425 million people are currently living with diabetes, prevalence is continuing to rise, and one in two people currently living with diabetes is undiagnosed.

Living with diabetes is a daily struggle, but many organizations have worked to create programs to decrease the struggle of those impacted. Ms. F, a 62-year-old African-American female with diabetes who relies on getting her nutrition from a food pantry, is a great example of someone that has benefited from these programs. Ms. F struggled with making proper food choices, adherence to proper medication, and transportation to make regular doctors’ appointments.

Through part of PCCI’s Connected Communities of Care program which shares patient’s information between providers and community-based organizations, Ms. F’s health and social service providers were able to connect and share information regarding her condition. When Ms. F visited the food pantry, staff members were aware of her diabetes. This knowledge enabled the staff to effectively guide her through her diet choices. This pilot program between three food pantries and Parkland Health & Hospital system helped many patients in taking the steps needed to control their disease.

In addition to limited access to healthy food choices, many patients in underserved communities have limited access to transportation. This challenge has made the remote monitoring of patients a critically important component in managing diabetes. PCCI is partnering with Parkland Health & Hospital System’s Global Diabetes Initiative to explore innovative approaches to improving the care of diabetic patients with foot ulcers which can lead to amputations if unresponsive to care. By acquiring data from home glucose monitoring devices and making real-time changes to treatment without physically having to see the patient, the (soon to be launched) study aims to create a sustainable remote glucose monitoring care system. This system will improve glucose control, promote faster healing of foot wound, and reduce long-term healthcare utilization and ultimately, reduce the burden cost of care for individuals and families.

Resources:

https://www.worlddiabetesday.org/

https://www.idf.org/e-library/epidemiology-research/54-our-activities/455-world-diabetes-day-2018-19.html

https://www.cdc.gov/diabetes/pdfs/data/statistics/national-diabetes-statistics-report.pdf

4 September 2018

Family Doc to “Design Doc”!




“Hey doc, don’t you miss being a family doctor?”  is a frequently asked question over cocktails and during client meetings. My response is always the same, “Actually, I am still serving patients but in a very innovative way and on a much larger scale for better health and social impact. I am now a “Design doc.”

Positive Promotion

After 15 gratifying years of service as a traditional family doctor, I now enjoy taking care of patients by designing healthcare solutions that result in better patient experiences, lower costs, and increased quality. My new career as a “design doc” has been very rewarding.

Design with a Cause

Like any designer, impactful ideas put both the big picture and intricate details into context simultaneously. Taking into consideration questions like: How does a doctor think? What are a patient’s expectations, needs, and goals? And what are the high-precision treatment options available? Begin the innovative process of designing scalable healthcare solutions.

Care and Collaboration

By approaching solutions from a “design doc” perspective, I collaborate with healthcare executives, frontline care teams, services providers, and members of the communities we serve to combine the “art” of medicine with clinically engineered artificial intelligence. The result of these collaborations and insights are solutions that can augment clinical decision making at the point of care and facilitate timely coordination of care beyond the walls of service providers and into the community.

Same Goal, Different approach

Quality of patient care has always been top of mind both during my time in the clinic and my new role at the Parkland Center for Clinical Innovation (PCCI). I went from providing patient care to enabling providers to better care for their patients and the community through PCCI’s innovative solutions.

Learn more about PCCI’s careers, or stay up-to-date with our recent news by following us on FacebookTwitter and LinkedIn!

Photo via Thinkstock by Getty Images. Item number: 857015410.

28 August 2018

My PCCI Internship – Synthetic Data Project




As my internship at Parkland Center for Clinical Innovation (PCCI) comes to an end, it feels great to look back and ponder over what I had the opportunity to work on, achieve and experience over the past three months. I arrived at PCCI with high expectations and am happy to say that I wasn’t disappointed. The project I worked on is called “Synthetic Data.” As the name suggests, the goal of the project is to create synthetic data from real medical datasets.

Why do we need synthetic medical data sets?

Real medical data is expensive and seldom released for research due to various privacy issues connected to it. Regulations exist because by looking at the medical data, a hacker could identify the name of a patient, thereby gaining access to sensitive information. The synthetic data project at PCCI aims to alleviate these problems by creating synthetic datasets, which are as close to the real medical datasets as possible without compromising a patient’s privacy.

Generative Machine Learning Algorithms and Challenges

Generative machine learning algorithms, specifically, Generative Adversarial Networks (GANs), proposed in 2014 [1] were used in this project. GANs have gained huge popularity within the machine learning community with a wide variety of GAN models being proposed.

There were quite a few challenges along the way in realizing the goal of synthetic data generation. First, the proposed GAN model has not been applied to real medical datasets before us, as it was mainly designed for image generation tasks. It also tends to not perform well with different modalities of data, which are naturally present in a real medical dataset. Modifying the network to work with real medical data or modifying the data (mostly getting it into a single modality) for it to work with GANs was a major challenge.

Second, there is an explosion in the number of GAN-based architectures being proposed and thus coming up with a novel architecture is a huge challenge in itself. After a lot of deliberation, we came up with an approach that allows us to incorporate domain knowledge into the GAN architecture. Below were our three possible approaches:

  1. Advice on just the discriminator.
  2. Advice on just the generator.
  3. Advice on improving the zero-sum game between the discriminator and the generator.

Improving the Zero-sum Game

Taking the third approach, we decided to incorporate reconstruction error [2] into the discriminator and the generator loss functions. The simple intuition is this: train the network with a mini-batch of data and generate the synthetic counterpart for each mini-batch.  Since reconstruction errors, as the name suggests, measure the errors between the real data and the “reconstructed” data, adding this to the loss functions can penalize either the discriminator or the generator depending on which performs the worse (i.e. has a higher loss difference) with respect to the mini-batch in question.

Initial Experiments Using the MIMIC III Dataset

For our initial experiments, we made use of the MIMIC III dataset [3], which is a dataset incorporating clinically relevant data for all admissions to an ICU at the Beth Israel Deaconess Medical Center between 2001 and 2012. Figure 2 shows the selected features.

Synthetic Data

Figure 2: Selected MIMIC III features

Experiments Using GAN-based Methods

We then ran a couple of experiments. First, we used three different GAN-based methods, without the reconstruction error, to see how the network performed with the defined loss functions.  Figure 3 shows the original and the generated data as obtained from these networks.

Synthetic Data

Figure 3: Snippets of the original and generated data

An Imbalanced Dataset

The original and synthetic datasets were then used to train a machine learning model. For the synthetic data to be useful, it needs to be as close to the real data distribution as possible, which should be captured by a machine learning classification model. Our dataset was highly imbalanced. We were predicting mortality rate and since a majority of patients come out of the ICU alive, we had nearly a 90%-10% split between negative examples i.e. patients who are alive after the ICU treatment and positive examples i.e. patients who die in the ICU. We used a cost-sensitive support vector machine classifier, constructed for such imbalanced data, to report the F1 score and the area under the curve (AUC-ROC) in Figure 4.

Synthetic Data

Figure 4: Comparison between the machine learning model performance for real and synthetic data. (W-GAN, GAN and MA-GAN are GAN models used to generate synthetic data)

As it can be seen, the results proved our hypothesis that real healthcare data was going to be a challenge for techniques, like GANs, which rely on many samples of very predictable data types since healthcare data tends to be more diverse and are difficult to compose into higher-order features. (Credit: David Watkins, my supervisor). We then used the reconstruction error to “indirectly” capture the relationships between the data points and used “real” hospital data to test on and create a synthetic dataset from it. The work was currently still in progress at the time this blog was written.

Working at PCCI

The work culture at PCCI, in my opinion, towers above other places. The work hours are flexible, the team’s ethics and bonding are strong and people are always willing to help you regardless of their schedule. The company truly values its employees and creates a work environment where every employee gives his/her best. You will never feel out of place (not even on the first day) as all your tasks are defined and everyone is so welcoming. A great thing about PCCI is the absence of an implicit hierarchy. Everyone from the CEO to your respective manager(s) (thank you Albert Karam) is always accessible. I never felt any different than a PCCI employee and this says a lot about the values of the company and how these values are being nurtured by PCCI’s CEO Steve Miff and all employees of this amazing organization.

Another important quality about PCCI is that it values and encourages all feedback that any employee may have and any grievances are then actually addressed. I am proud to say that Steve himself makes sure that any such issues are addressed.

Nothing is perfect and PCCI has a few areas where it can improve. One area of improvement is getting access to the real data. This is currently a very is a slow process, which makes sense as it is sensitive medical information of real patients, but it can be sped up. Another area of improvement, that I have actually raised to Steve during a meeting is that PCCI should focus on publishing research papers. It is a company that is capable of doing amazing research and has access to real medical datasets that are difficult to find. I hope PCCI becomes more active in this regard.

What PCCI does is super important to the community and it makes sure that all the employees realize this fact. Creating an impact in the real world, on real lives is a great morale booster for anyone and since PCCI values are centered around this motto, working here has been a great experience. It’s been a pleasure interning at PCCI and I am happy to be taking fond memories with me back to school.

Learn more about PCCI’s careers, or stay-up-to-date with our recent news by following us on FacebookTwitter and LinkedIn!

References:

[1] Goodfellow, Ian, et al. “Generative adversarial nets.” Advances in neural information processing systems. 2014.

[2] Borji, Ali. “Pros and Cons of GAN Evaluation Measures.” arXiv preprint 2018.

[3] Johnson, Alistair EW, et al. “MIMIC-III, a freely accessible critical care database.” Nature 2016.

8 August 2018

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.

31 July 2018

Prevention of Sepsis Through Machine Learning




Sepsis is a word that can evoke feelings of dread for most people and yet, many fail to understand its meaning and implications fully. It is a life-threatening condition that arises when the body’s response to infection results in organ dysfunction or failure. It is one of the leading causes of deaths with 6 to 9 million deaths per year worldwide and 250,000 deaths per year in the United States. To make matters worse, sepsis is also one of the leading causes of preventable deaths worldwide. The key to prevention is early detection since every hour of delay increases the odds of death by 20%. However, sepsis can be very hard to detect especially in its early stages when it is reversible. The symptoms often point to other conditions and patients can deteriorate rapidly. Traditional risk models such as SIRS criteria to detect sepsis generate a lot of false positives leading to inefficient or ineffective care and nursing fatigue.

sepsis

Leveraging Machine Learning 

Due to PCCI’s close collaboration with Parkland Health & Hospital System, we recognized sepsis detection and prevention as an area that could benefit from an advanced machine learning-based algorithm. To be effective, the model needed to fulfill the following criteria:

  • A high enough positive predictive value (PPV) to ensure that false positives do not end up causing frustration for clinicians. It is very challenging to have a high PPV for use cases with low prevalence rates in the target population. Despite the high mortality numbers, the overall prevalence rate for sepsis in an inpatient setting (our target population) is 3% to 4%.
  • A prediction interval (how far ahead in the future can the model predict) that gives clinicians a sufficient heads-up to intervene.

PCCI developed a predictive model to fulfill the criteria identified as effective. This real-time model is designed to predict an individual’s risk of becoming septic within the next 12 hours.

Instead of relying on traditional ways of detecting the risk of sepsis through a handful of physiologic variables, we cast our net wide and started out with approximately 120 variables as potential predictors, such as socio-demographic, vital signs, co-morbidities, hospital utilization, medical conditions, clinical history, and lab results.

Testing the Lasso Logistic Regression Model

We trained and tested a Lasso Logistic Regression model, a decision tree and a neural net on 27 months-worth of Inpatient encounters from Parkland. Initial data sets included 54,629 encounters and 30,922 patients. We chose the Lasso Logistic Regression model as the final model because of its high performance and interpretability by clinical users. The resulting model that compared favorably to other community models resulted in:

  • 95% Accuracy
  • 30% PPV
  • 55% Sensitivity

Incorporating the Model into Clinical Workflows

A model is useful only if it is actionable and available at point of care. PCCI’s Sepsis model is incorporated into clinical workflows through industry-standard APIs. The model accesses EHR data in real-time every five minutes and alerts the clinician if the risk is above a certain threshold. The risk threshold can be tailored to local populations and clinical best practices of an organization. The alerting mechanism can vary based on EHR functionality, but it can include a simple alert, queuing up of order sets based on the risk threshold, and sending a real-time alert to a secure mobile device or pager for a member of the rapid response team.

Learn more about PCCI’s work, or stay up-to-date with our recent news by following us on FacebookTwitter and LinkedIn!

25 July 2018

8 Takeaways from the West Coast Payer and Provider Summit Addressing SDOH for Complex Populations




While the importance of addressing social determinants of health (SDOH) is now a common theme in reputable conferences, learnings are growing richer and more intense. In June, The West Coast Payer and Provider Summit to Address Social Determinants of Health for Complex Populations was an industry gem hosted in Scottsdale, AZ. Here is a recap of what I felt were some of the biggest takeaways from the summit.

1. Purpose Driven Change-Leadership Workshop by David Shore, PhD, Harvard

Throughout the summit weekend, many workshops were presented by thought-leaders in the space. David Shore’s workshop was a veritable delight of new twists on old themes to jog the mind and start a new race for transformative change within one’s sphere of control. Some key points included:

  • Spending extra time shaping questions to ask increases the efficiency at arriving at solutions
  • Project life cycles should be front-end loaded with interrogations of reality and refuting assumptions
  • Conduct a sequence of smaller projects that feed into a cohesive program instead of long drawn out projects
  • It’s only innovation if you effectively solve meaningful problems, which you can scale and spread
  • Sustain with the “Science of Spread” methodology
  • According to research, the optimal size of a project team is seven to eight people – if it takes more than two pizzas to feed your team lunch, you have too many people!
  • Many interesting points of view of healthcare providers regarding SDOH
    • While 40-50% see their important influence on outcomes, 70-90 % don’t necessarily think it’s their job to respond to those needs.
  • A personal favorite: go beyond lessons learned to lessons leveraged!

2. Extensions of the Triple Aim Statement Reframing the Importance of SDOH

First, we had the Triple Aim, then quadruple and now… the quintuple aim:

  1. Cost
  2. Quality
  3. Patient Experience
  4. Provider experience
  5. Equity\SDOH

As this Triple Aim Statement continues to expand, what do you envision to be the sixth?

3. Social and Healthcare Platforms

Early stage entrants working on cloud platforms to connect care, patient created and social data are seeing encouraging early gains. Below are some notable platforms to keep an eye out for:

 The Real-World Education Detection and Intervention (REDI) Platform:

  • Currently deploying in border towns along southwest Texas by UT Austin Lynda Chin, MD’s team in collaboration with PWC (pro bono), AWS, and Walmart
  • They report a 1.7% decrease in Hgb A1 c of diabetics in an integrated data sharing program with remote monitoring

ORCHWA Platform:

  • An Oregon 1115 Waiver project is driving to get large numbers of community health workers across the state to document on and create closed-loop referral
  • They focused more on the human aspects of this and it seems that they may still be in technology development

4. Powerful Visualizations for Action

This was a “blow you away presentation” with some truly powerfully meaningful novel approaches driven by Jason Cunningham, MD, CMO of West County Health Centers. Below are suggested steps one can take to innovate the virtual world of healthcare:

  • Use a mix of vendors to include Tableu, Unifi + KUMO + Argis
  • Create visualizations for actions. For example, zip code areas affected by wildfires were targeted and cross referenced with their patient list allowing the ability pinpoint their patients for proactive outreach
  • Allow for early identification and replacement of lost belongings including medications, medical supplies, and strong patient experience feedback approval

5. Early findings and Interesting Metrics to Prove the Value of SDOH Intervention

While the consensus opinion and extensive research clearly indicate the magnitude and causal nature of SDOH’s influence on health outcomes, quality, and cost, most interventions depend on unique funding streams. This is because ROI hasn’t been proven to hit mainstream reimbursement.  Examples include:

  • WellCare Insurance Plan reported a decrease of $2,400 per year per member for those who received social needs interventions versus those who did not
  • Sutter Health used a Health Equity Index to target risk populations affected by disparities and used the index to prove intervention effects
  • Kaiser Permanente created a patient “feelings of hope” scale
  • Special Needs Plans (SNP) used a “Loneliness scale,” which contributed to disease progression and longevity to target and monitor at-risk individuals

Return on investment is largely focused on health outcomes, but how can we measure the social outcomes of Social ROI?

6. Speeding Up Patient Transport

Getting patients where they need to go, when they need to go is a top priority that has an impact on not only outcomes but patient experience in terms of ease and convenience. Just think about your own stress when your car is in the shop, stress can agitate any clinical state. Interesting approaches to speeding up patient transport include:

  • Ordering patient transport through referrals in their EHR
  • Superimposing public transport routes onto patient location density and using the information to advocate for new routes

7. New Term – “Patient Disengagement”

Patient engagement often is a “catch-all” bundled term. But new ways of disentangling the terms unlocks possibilities, such as:

  • Disengagement Vulnerabilities- a method of enumerating characteristics of individuals and their circumstances that can interfere with engagement to target and develop personal connectedness
  • Tangible incentives are used to increase participation and encourage healthy choices

8. Payer Pressing Mobile Engagement for the Homeless Who Are “Not Ready to be Housed”

“Housing first” advocates began changing the landscape and the dialogue on the all-too-common reality of homelessness. One size doesn’t fit all in this multidimensional problem. A notable example is the homeless and housing resource team created by ANTHEM Indiana Medicaid. If patients aren’t ready to be confined by walls, the program provides a cell phone and a mobile app to engage them with online tools.

Learn more about PCCI’s collaborations, or stay up-to-date with our recent news by following us on Facebook, Twitter and LinkedIn!

20 July 2018

Obtaining Results in Population Health Management




THREE FUNDAMENTAL ELEMENTS

True population health management requires at least three fundamental elements to drive transformational change and meaningful results:

  1. Aligned incentives across payers, health systems, post-acute care providers, and physicians.
  2. Technology and framework fit for engaging an entire community that leverages resources for care coordination and addressing social, economic, and behavioral needs
  3. Personal engagement to drive activation, behavioral modification, and create a foundation for shared decision making.

HOW ASTHMA AFFECTS POPULATION HEALTH

While this framework is required for managing all populations, it becomes critical and complex when managing chronic disease. For example, asthma is a common disease, but it’s rarely recognized as one of the most common chronic diseases for children under the age of 18 with 6.2 million affected individuals. Over 8% of children have asthma, most with symptoms occurring before five years of age. Asthma disproportionately affects low-income, minority, and inner-city populations with African-American children being at the highest risk. It is a significant driver of school absenteeism, with an estimated 12-15 million school days lost per year.

Asthma impacts both families and the healthcare system financially as well as socially. Childhood asthma is the cause of nearly five million physician visits and more than 200,000 hospitalizations each year. Medical expenses for a child with asthma are almost double than those for a child without the disease. Given these statistics, there’s a compelling need for early identification and effective intervention to control this disease.

PCCI’S POPULATION HEALTH FRAMEWORK

The Parkland Center for Clinical Innovation (PCCI) has developed sophisticated predictive models to proactively identify children at risk for asthma exacerbations and has combined this powerful engine with a comprehensive population health framework to:

  • Reduce asthma emergency department visits and hospitalizations
  • Increase patient adherence to medication and clinic visits
  • Increase evidence-based leading practices at the provider level

 

Figure 1 Highlights the PCCI Pediatric Asthma Framework

Figure 1. PCCI Pediatric Asthma Framework

PCCI’S PEDIATRIC ASTHMA MODEL AT WORK

Through tailored clinical workflows, monthly provider reports, point-of-care EHR integration, and patient-centric mobile messaging applications, the framework can engage providers, communities, patients, and their families to optimize care, drive engagement, and reduce unnecessary utilization.

Within three years, deployment of the program in the Dallas metro-area by a large health plan resulted in:

  • 32-50% increase in the appropriate use of controller medications
  • 31% reduction in ED visits
  • 42% reduction in asthma-related inpatient admissions

This framework has resulted in more than a 40% drop in the cost of asthma care with the health plan saving over $18 million for both patients and healthcare providers.

ENGAGEMENT IS KEY

The key to PCCI’s pediatric asthma framework is that the clinicians, patients, and health systems are all engaged which generates value for all parties involved. With a foundation in literature-based evidence, the framework aligns with national and international guidelines. It is also both modular and patient-friendly – offering different levels of interventions based on patient risk score, needs, and available resources.
By utilizing our sophisticated predictive model to identify children at risk for asthma proactively, we are able to combine it with a comprehensive population health framework to:

  • Increase patient adherence to medication and clinic visits
  • Educate patients in care and self-management
  • Optimize health plan to care manager outreach and workflow
  • Engage physicians via direct EHR alerts
  • Reduce preventable asthma ED visits and hospitalizations

CURRENT DEPLOYMENT

Twenty-one community clinics in the DFW area receive real-time alerts embedded in their EMR and monthly progress reports. These activities resulted in 32% to 50% improvement in asthma controller medication prescriptions and a 5% improvement in the asthma medication ratio (a HEDIS metric). Some clinics are using the reports to redesign asthma care delivery programs and roll out shared medical appointments as needed, while others use the reports to guide spirometry scheduling.

ENGAGEMENT WITH THE POPULATION HEALTH FRAMEWORK

High and very high-risk patients can engage through succinct, precise, and educational text messages delivered by a simple effective mobile platform. Patients are surveyed about their condition, emergency inhaler usage monitored, and they are reminded of upcoming appointments and medication refills. These innovative features allow continuous symptom monitoring by the clinician to ensure continuity of care and positive outcomes. Patients have displayed satisfaction with the program, with over 70% top box satisfaction and an attrition rate of less than 15% on mobile engagement over a two-year period.

Community engagement is a critical element when trying to ensure not only coordination of care, but referrals and connections to community resources. Providers at either hospitals or the clinics, receive best practice alerts and utilize technology to identify SDOH needs. They can also refer families to community-based organizations (CBO) for assistance with critical daily needs. Currently, we’re in the process of expanding interactions and engagement with local schools, so that school nurses concurrently receive alerts on high risks children and can help coordinate care in those settings.

COMPETING POPULATION HEALTH FRAMEWORKS

While there are multiple and broad initiatives occurring in every market, results have displayed limited to incremental progress. PCCI has demonstrated that transformational change and meaningful results are achievable. Meaningful results require concurrent engagement and coordination of payers, providers, community, and patients via advanced risk-predictive stratification algorithms and deployment of information via new/updated workflows at the point of interaction. Figure 2 highlights the difference and impact our comprehensive program has across a market.

 

Figure 2. PCCI Pediatric Asthma controlled analysis. Comparison 1: DFWHC Medicaid <18 yo: 5% drop in asthma ED visits. Comparison 2: All Health Plan Members <18 yo: 10% drop in asthma ED visits. PCCI Asthma Program: 31% drop in asthma ED visits

Additional Innovations

Despite tremendous success, opportunities still exist to improve results and continue to further engage providers and patients. We are designing and rolling out two additional innovation pilots:

  1. Testing the effectiveness of disease-specific in-home personal assistance devices (Amazon Echo) to engage groups of homogeneous, high-risk, pediatric asthma children in a gamified home environment.
  2. Integrating within a home or community to allow remote monitoring of asthma medication adherence and in-home air quality by using “Internet-of-Things” integration.

The framework is designed with adaptability in mind and is ideal for environments where providers hold risk-based contracts. Future applications will include other patient populations and health conditions. PCCI’s Pediatric Asthma Population Health Framework has not only reduced unnecessary utilization and costs, but it has also improved the healthcare experience for hundreds of pediatric patients and their parents.

Central to the work of PCCI is its strength in predictive analytics/modeling and building connected communities using intelligent, integrated, electronic information exchange platforms. Our state-of-the-art programs and advisory services deliver exceptional value to Parkland Health & Hospital System, the local community, and the broader healthcare market.

Learn more about PCCI’s collaborations, or stay up-to-date with our recent news by following us on Facebook, Twitter and LinkedIn!

13 July 2018

DISD Students Share Mobile App “Klinik” with PCCI




Students Recognized for Innovation

Recently, PCCI had the opportunity to welcome students from two Dallas ISD schools– Townview Science and Engineering Magnet and Emmett J. Conrad High School. This group of students are finalists in the Lenovo MIT App Developer Competition and have been invited to Washington D.C where they will be recognized in an awards ceremony for their innovative app aimed at addressing social and health needs in their communities.

A Different Type of App

Nine Dallas ISD students were given the opportunity to design an app for a competition. While most kids would opt for creating a gaming app or the next social media hit, these students took a different approach. Two-thirds of their classmates come from economically disadvantaged families, and many of their friends, neighbors, and family members have little access to food or healthcare resources. Deeply rooted in their passion for helping their community, they decided to develop “Klinik” an app that could enable them to address these immediate needs.

What is Klinik?

Klinik” is a mobile app that provides users with crucial information on how to get access to basic resources such as food, healthcare, and shelter. The team of prepared and articulate students shared their project with PCCI innovators, spurring conversation around various aspects of app development. By brainstorming effectively, canvassing their communities to create a database of resources, and having potential users provide feedback on the design during development, these students were able to create an app with a sophisticated design and scope mirroring methods seen in well-established companies.

Learning how to compile accurate community resource inventories that enable those who need them according to their preferences is an active area of research in the field and a strong focus for PCCI. The students having developed the basic app are assessing how it can be scaled for broader use across geographies.

PCCI Strategizes with DISD Students

PCCI will strategize with DISD Academy programs and NAF Future Read Lab to create opportunities for collaboration and learning during innovation cycles and to extend the applicability of initiatives. We think that these students and their peers have the potential to work closely with PCCI through worksite tours, internships and a variety of other NAF sponsored activities.

Their work was sponsored by the Lenovo App Inventor program. Lenovo Scholars provides STEM education through NAF academies in the U.S. in partnership with the Massachusetts Institute of Technology (MIT) to enable the next generation of app developers.

Learn more about PCCI’s collaborations, or stay-up-to-date with our recent news by following us on FacebookTwitter and LinkedIn!

12 June 2018

The Increasing Importance of Social Determinants of Health




IMPACT ON HEALTH OUTCOMES

Over the last few years, it has been very clear from research that Social Determinants of Health (SDOH) variables have a major impact on health outcomes. It is estimated that close to 80% of health outcomes are impacted by SDOH. With the rise of population-based risk contracts in both the commercial and government sector, it is essential for both providers and payers to collaborate in the identification of best practices to address these SDOH variables. This is especially relevant as providers such as hospitals assume greater risk in arrangements with plans throughout the country such as Accountable Care Organizations (ACO) and bundled payments.

NATIONAL INTEREST AND PROGRESS

Many national associations such as the American Hospital Association (AHA) and America’s Essential Hospitals have developed resources and launched learning collaboratives for hospitals and health systems to address these variables such as food insecurity, housing, and transportation. Health system innovation and care-redesign models driven by organizations such as Healthbox and AVIA have launched collaboratives and forums to educate and address SDOH initiatives. The May 3, 2018, Healthbox forum discussion on “Challenging the Status Quo of Social Determinants” visually captured the opportunities and challenges ahead into one image (Figure 1):

Social Determinants of Health

Figure 1: Image captured during Healthbox Executive Panel Discussion, May 3, 2018. Chicago, IL

These variables have always been a focus of many health systems in terms of articulating their benefit to the community, but now they have particular importance given the rise of more population risk contracts.

Several major barriers have impeded the industry’s progress in addressing SDOH variables: funding and regulations. Fortunately, we have begun to see opportunities in both areas emerge in 2018!

MEDICARE UPDATES AND THE BENEFITS OF SOCIAL DETERMINANTS OF HEALTH DATA

Medicare Advantage (MA) has a regulation titled “Uniformity Standard” that requires all of the plan’s benefits, including cost-sharing, be the same for all plan enrollees. On April 2, 2018, the Centers for Medicare & Medicaid Services (CMS) outlined several widespread changes in this regulation that both providers and plans have advocated for over the last several years in their 2019 Medicare Advantage Call Letter. CMS expanded the flexibility of lifting the uniformity of supplemental benefit to allow different segments of an MA plan to offer specific benefits to a targeted population like diabetics. This can begin in CY 2019 (January 1, 2019) after the plan designs are approved by CMS. An example could be reduced cost sharing for foot or eye exams. In their official bids that were submitted by the June 4, 2018 deadline, the MA plans can include any of these supplemental benefit elements. Hopefully, providers will see many of the plans deciding to include these additional benefits in their MA bids to address the SDOH variables.

Additionally, in the Bipartisan Budget Act (BBA) that was passed in early 2018, Congress has taken it further by extending the lifting of the uniformity of the supplemental benefits to all chronically ill members of the MA plans effective January 1, 2020. This reinforces the need for us to gain valuable lessons during 2019 in order to determine what works and what doesn’t before it is transitioned to a broader population.

The Chronic Care Act of 2018 extended the Center for Medicare & Medicaid Innovation’s (CMMI) Valued-Based Insurance Design Model to all 50 states in 2020. This model was launched in 2017 to allow Medicare Advantage plans to offer supplemental benefits and reduced cost-sharing to seven conditions including Coronary Artery Disease or Congestive Heart Failure. The model focuses on four approaches:

  1. Reduced Cost Sharing for High-Value Services
  2. Reduced Cost Sharing for High-Value Providers
  3. Reduced Cost Sharing for enrollees participating in disease management
  4. Coverage of additional supplemental benefits such as transport or meal delivery

The creation of more supplemental benefits will enhance the quality of services we provide for our patients especially in terms of addressing the SDOH. Encouraging the inclusion of these targeted supplemental benefits will allow us to partner with payers to improve the health of the country in a more innovative way.

ADDRESSING SDOH WITH HEALTHCARE PROVIDERS AND COMMUNITY RESOURCES

At PCCI, we have been directly involved in national and state-driven education forums, presentations, and roundtables directed to design and deploy local models for the Connected Communities of Care program (previously known as the Information Exchange Portal) that bring together providers, payers, philanthropic organizations, community-based organizations (CBO), and local/state government entities. While most markets continue to be in a learning mode, significant and tangible activities are being initiated in a number of municipalities, including Dallas, Raleigh-Durham, Louisville, Detroit, Chicago, Phoenix, Salt Lake City, as well as across whole regions. For example, North Carolina recently requested proposals for the development of a North Carolina Resource Platform via the Foundation for Health Leadership & Innovation. The goal of this multi-year program is to connect over 3,000 statewide community-based organizations via technology, and facilitate SDOH. This will be completed through a programmatic coordination of referrals between healthcare providers and community resources to comprehensively identify and address the needs of individuals across the state. On a broader level, the Accountable Health Communities Model deployed in 2017 is engaging 31 organizations across the country to address a critical gap between clinical care and community services in the current healthcare delivery system. This is being done by testing the process of systematically identifying and addressing the health-related social needs of Medicare and Medicaid beneficiaries through screening, referral, and community navigation services to see if it will impact healthcare costs and reduce healthcare utilization.

SUCCESS IN SIX TRACKS

Our experience over the last five years across Dallas tells us that models will need to address six tracks to be successful: Governance, Legal, Technology, Clinical Workflows, CBO Workflows, and Sustainability (Figure 2). The maturity and evolution of the models need to develop and be staged within a multi-year deployment framework (concentric circles in Figure 2 represent the progression and evolution of the model with outer circles representing mature and more sophisticated models).

Social Determinants of Health

Figure 2: Connected Communities of Care program multi-year deployment framework

There is also a critical upfront readiness and deployment/implementation assessment that is important in order to stage the deployment of a Connected Community of Care program. This broad representation of the community’s fabric is critical to ensure that:

  1. A community is ready to undertake the operational and financial requirements associated with deploying a Connected Communities of Care program
  2. The healthcare and social needs of the community are at the forefront of the customized design of the platform (something most for-profit technology vendors offering an out-of-the-box solution either cannot do or fail to do properly)
  3. The design is sufficiently flexible to adjust as the healthcare or social needs of the community change

Addressing SDOH is finally moving from a “buzz” word to implementation pilots. While we talked a lot about population health over the last 10 years, doing population health without a truly engaged and “Connected Community of Care” is like focusing on rescuing people from drowning in a river vs. building a bridge so they can cross it safely. As we continue this journey, let us make sure we build a bridge that adapts to the needs of each community and has emerging local and national models of care to ensure sustainability. We don’t want to end up with a bridge like the Choluteca Bridge in Honduras, connecting nothing to nowhere.

Acknowledgments: Valinda Rutledge, PCCI Executive Advisor and Lindsey Nace, PCCI Marketing and Communications have contributed to this article.

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