Parkland RITE program targets prevention of hospital infections and improved sepsis care

Hospital-wide effort reduces infection rates, saves lives

DALLAS —Each year sepsis strikes approximately 1.7 million people in the U.S. and more than a quarter million die from the condition, making it a major cause of death. Healthcare-associated infections (HAI) contribute to about 25 percent of these deaths. According to infection prevention experts, many hospitals are trying different approaches to reduce healthcare-associated infections, improve care of patients presenting with sepsis, and save lives. In 2013, Parkland Health & Hospital System launched an innovative hospital-wide program to reduce HAI and sepsis-related deaths, called RITE (Reduce Infections Together in Everyone).

In the first five years, the multi-disciplinary program has achieved impressive results, reducing rates of infection each year since the program was launched. The net estimated impact of the RITE program is more than a thousand infections prevented and approximately $17,781,000 in cost avoidance.

“Providing quality care begins with providing safe care,” said Parkland’s Chief of Infection Prevention, Pranavi Sreeramoju, MD. “We targeted catheter-associated urinary tract infections and central line-associated blood stream infections hospital-wide, surgical site infections following eighteen different types of surgical procedures, and patients presenting with signs and symptoms of sepsis to our emergency department.”

Parkland’s prevention approach centered on standardizing care of patients at risk for these complications; engaging healthcare personnel by talking to them and exploring barriers to adoption of best practice; standardizing curriculum on how to prevent HAI and improve sepsis care; use of medical informatics tools such as early warning system for sepsis developed by Parkland Center for Clinical Innovation; use of best practice alerts and order sets in the electronic medical records; and improving workflows.

According to Dr. Sreeramoju who is also Associate Professor of Internal Medicine at UT Southwestern Medical Center, Parkland’s RITE initiative forged a new approach to prevention by leaning on one part medical science and two parts social science.

“It’s been said about infection prevention that we know what to do – that’s the medical science. The biggest challenge for hospitals remains getting everyone to do the right thing, all the time,” she said. “Something as basic as hand hygiene requires constant vigilance in a hospital setting. So we decided to focus on identifying the most effective ways to influence behavior and make best practices easier to adhere to.

“We took a ‘high touch’ approach to working with staff, spending time analyzing interactions among multi-disciplinary caregivers, and we gave front-line staff the opportunity to provide input that could help us improve our infection prevention strategies,” Dr. Sreeramoju explained.

The scope of the RITE initiative is massive. Parkland Memorial Hospital has more than 40,000 inpatient discharges and 244,000 emergency department visits annually. Approximately 2,500 patients present to Parkland’s ED with suspected sepsis each year.

During Sepsis Awareness Month in September, organizations like the Sepsis Alliance, one of the nation’s leading sepsis patient advocacy groups, hope to increase public and healthcare professionals’ knowledge about this dangerous and vexing health risk. In a 2016 report, the Sepsis Alliance stated that “even though hospitalizations are increasing, a majority of Americans still don’t know what sepsis is or how to treat it.” The most recent Sepsis Alliance Awareness Survey found that less than one-half of all adult Americans have ever heard of sepsis. And the number is even lower among younger adults.

To learn more about services at Parkland hospital, visit www.parklandhospital.com

Contact

Parkland Health & Hospital System
Catherine Bradley
469-419-4400 catherine.bradley@phhs.org

PCCI
Mike Crouch
214-590-3887 Michael.Crouch@PCCInnovation.org

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 Facebook, Twitter and LinkedIn!

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

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 Facebook, Twitter 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.

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.

The Tech Tribune: 2019 10 Best Tech Startups in Dallas

The Tech Tribune staff has compiled the very best tech startups in Dallas, Texas. In doing our research, we considered several factors including but not limited to:

  1. Revenue potential
  2. Leadership team
  3. Brand/product traction
  4. Competitive landscape

Additionally, all companies must be independent (un-acquired), privately owned, at most 10 years old, and have received at least one round of funding in order to qualify.

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 Facebook, Twitter and LinkedIn!

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

While the importance of addressing Non Medical Drivers of Health (NMDOH) 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 Non Medical Drivers 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 NMDOH
    • 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 NMDOH

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

  1. Cost
  2. Quality
  3. Patient Experience
  4. Provider experience
  5. EquityNMDOH

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 NMDOH Intervention

While the consensus opinion and extensive research clearly indicate the magnitude and causal nature of NMDOH’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!