12 September 2019

Texas Medicine Magazine highlights success of one clinic’s allergy and asthma pilot program




The September issue of the Texas Medical Association’s magazine, Texas Medicine Magazine, featured the efforts of C. Turner Lewis, III, MD, Medical Director of Children’s Medical Clinics of East Texas, to mitigate the harmful effects of pediatric asthma and alergies. Dr. Lewis employed a pilot program that included elements of PCCI’s predictive modeling to help reduce emergency department visits to zero over the course of a two-month period.

Click on the image below to read the entire article:

 

29 July 2019

Deep Learning Model to Predict Pediatric Asthma Emergency Department Visits




Pediatric asthma is the most prevalent chronic childhood illness, afflicting about 6.2 million children in the United States. However, asthma could be better managed by identifying and avoiding triggers, educating about medications and proper disease management strategies.

Parkland Center for Clinical Innovation (PCCI) has been working with the Parkland Community Health Plan (PCHP) for the past four years to help them with timely identification of high risk asthma members in the pediatric population. We have deployed a logistic regression model on claims data that is the foundation for an innovative care redesign and text based patient engagement program that has show consistent cost savings and clinical outcomes improvement for PCHP members. You can learn more about this program here.

Thus, this issue for us is not merely an academic exercise. We realize that a predictive model with improved statistical performance can yield even better health outcomes for these families and improved cost savings for PCHP. Keeping that in mind, we have regularly looked for ways to continue to improve our model that is not just limited to retraining the model but also finding ways to improve data quality and bringing in additional data sets for workflow improvement. More recently, we decided to retrain the model using deep learning techniques — more specifically using an ANN.

Our deep learning model produced an AUC of 0.845 which was only slightly better than the AUC of the current logistic regression model at 0.842.

You can read about details of our work at arXiv here.

Healthcare has high expectations for the level of transparency from machine learning algorithms deployed in a clinical setting. Deep learning models with their relative lack of transparency are not always the best contender for those situations. However, if the improvement in performance of the model for relevant statistical measures is significant enough, then there can be a strong case for deployment of a deep learning model over say, a logistic regression model.

However, in this case, with the exact same data set and initial feature list, ANN model only produced slightly higher statistical classification power than the Lasso logistic regression. This is consistent with the results from some other published research that has compared logistic regression and ANN models in multiple medical data classification tasks. This study further confirmed that the Lasso logistic regression model developed by PCCI in 2015 could produce desirable statistical performance that is non-inferior to deep learning models which are more difficult to interpret. And in order for our predictive models to be deployed and effectively improve patient care, we need to work closely with clinicians to explain predictions in comprehensive and interpretable formats to build trust and transparency with stakeholders.

For future studies, blender algorithms would be tested against other singular models to achieve better statistical performances. We would explore the temporal relationships in claims data using other deep learning models, like Recurrent Neural Networks (RNN) and Long Short-Term Memory (LSTM).

Parkland Center for Clinical Innovation (PCCI) uses data, advanced analytics and Artificial Intelligence to help individuals lead healthier lives.

15 November 2018

Dallas Medical Journal: Powerful predictive analytics and mobile technology improve the lives of children with asthma in Dallas




Dallas Medical Journal: Powerful predictive analytics and mobile technology improve the lives of children with asthma in Dallas

23 August 2018

Healthcare Informatics: PCCI Combines Predictive Modeling, Patient Engagement to Address Pediatric Asthma




Over three years, effort leads to 31 percent drop in ED visits and 42 percent drop in admissions for pediatric asthma cohort

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.

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