pediatric asthma Archives – PCCI

20 October 2021

Parkland program helps pediatric patients with asthma management




Asthma a top cause of sickness in Dallas County children

DALLAS – It’s difficult to keep Sir Moreland of Mesquite indoors. Like most 12-year-old boys, Sir loves spending time outside with his brother and friends. Playing baseball and basketball is his favorite thing to do but right now his focus is flag football.

“This is the first time I’ll be playing for a team,” said Sir. “I’m scared, nervous and excited.”

Playing sports was not always easy for Sir. At age 5 after running outside with friends, Sir began struggling to breathe and was rushed to the Emergency Department at Children’s Hospital. His mother Sheniqua Turner, 36, had no idea the symptoms her son was experiencing at that moment were due to an asthma attack. He was hospitalized for three days.

“I knew of some kids who had asthma, but I’d never seen an asthma attack firsthand,” said Turner. “I didn’t know what was going on. I was really nervous and didn’t know what to do.”

According to physicians at Parkland Health & Hospital System, asthma symptoms vary from person to person. The most common include shortness of breath, chest tightness or pain, coughing or wheezing and episodes that worsen with respiratory viruses like the flu. These symptoms tend to appear when exposed to triggers like pet dander, dust, pollen, air pollutant, mold or even cold air.  For some, the symptoms might not necessarily be conspicuous, such as a mild, prolonged cough.

After her son’s discharge from the hospital Turner immediately followed up with Sir’s pediatrician at Parkland who educated the worried mother about asthma and potential treatments. She left with a personalized action plan to help manage Sir’s asthma.

“I had to learn all his triggers,” said Turner. “I think that’s the reason he hasn’t had an asthma attack since. He’s doing really good now.”

About 6 million children in the U.S. ages 0-17 years have asthma, according to the Centers for Disease Control and Prevention. The 2019 Dallas County Community Health Needs Assessment (CHNA) identified asthma as a leading chronic disease among children, particularly in children residing in ZIP codes located in the southeast of Dallas County. Parkland providers have launched a new program to educate parents and other caregivers and stress the importance of having a personalized action plan to help manage the disease.

“It’s a significant problem. Children would visit their nurse at school because they didn’t have their asthma under control,” said Cesar Termulo, MD, Associate Medical Director at Parkland’s Hatcher Station Community Oriented Primary Care health center. “At times their case would be too severe, and they would need to be taken to the hospital. The majority of these children were not being seen by a primary care doctor to help manage their asthma.”

To help families dealing with the condition, six ZIP codes in Dallas County (75210, 75211, 75215, 75216, 75217 and 75241) were identified to target with interventions to improve children’s asthma control through Breath for Life & Learn for Life, a collaborative effort between Parkland and multiple organizations to address asthma in the community.

Parkland Center for Clinical Innovation (PCCI) instituted an educational text messaging program that focuses on upstream interventions to engage and improve patient care in identified ZIP codes such as patient symptom and medication adherence monitoring. The text messaging program allows for two-way communication. For example, the parent may receive a text message asking, “How is your child’s asthma today?” If the response is the child is experiencing some difficulties, PCCI will notify their provider who may recommend the parent to seek care. The data-driven model assists with care prioritization by referring patients to their primary care physician for asthma management when indicated. If they do not have a primary care physician, they are referred to Parkland to establish a medical home for primary care to include asthma medical management.

PCCI’s asthma risk-prediction model remotely monitors background electronic data of high-risk asthma children.  These children may be referred to their primary care physician.  If the physician requires additional information, the child can be referred to Dallas County Health & Human Services (DCHHS) for a home visit.  DCHHS reaches out virtually to assess their current asthma status and identify environmental factors at home.   Based on their findings, DCHHS community health workers recommend changes to the home environment to reduce exposure to asthma triggers.

“The pediatric asthma model retains a good prediction ability and provides additional clinical insights not previously available using claims data only,” said Aida Somun, PMP, MBA, Chief Operations Officer at PCCI. “With the addition of electronic health records data, our asthma model can be used for all children irrespective of insurance status, thus expanding the benefits of our program to more vulnerable children with asthma.”

Positive Breathing, an organization with a mobile bus that has been outfitted to perform advanced asthma spirometry screening, will also provide outreach into the hard-to-reach sectors of the community and refer patients who are symptomatic.

There are plans for Dallas Independent School District to also refer students with asthma who do not currently have a primary care physician.

“The goal is to reduce avoidable asthma-related visits to the ED and hospitalizations through community outreach,” Dr. Termulo said. “We can make a huge difference.”

Sir says he feels “really good” now that he has his asthma under control. “I don’t have to worry much about it anymore. I can run as fast as I can.”

“Asthma is a real monster, but it’s possible to overcome it. It’s all about educating yourself,” said Turner.

If you live in one of the targeted zip codes and would like to enroll in the asthma text messaging program, please text @asthma to 844-721-0839. For Spanish, please text @asma1 to 844-721-0839.To find out about services at Parkland, go to www.parklandhospital.com. For more information about the 2019 Community Health Needs Assessment go to www.parklandhospital.com/chna .

# # #

 

 

4 May 2021

World Asthma Day: How PCCI’s predictive model helped improve care low-income children with asthma in Dallas




As part of May’s Asthma Awareness Month and World Asthma Day (May 4), PCCI is presenting its work, partnering with Parkland and the Parkland Community Health Plan, where its platform supporting pediatric asthma has helped thousands of children, dramatically reduced hospital visits and resulted in millions of dollars in cost savings. Following is an overview of the pediatric asthma programs that PCCI has played a key role in developing.

##

How PCCI’s predictive model helped improve care low-income children with asthma in Dallas

By Yolande Pengetnze, MD, MS, FAAP,
Senior Medical Director, Parkland Center for Clinical Innovation

Bringing together advanced data science and clinical expertise to help at-risk populations is a primary mission at PCCI and the results derived from our program to help improve care and outcomes for children with asthma, demonstrate the effectiveness of this approach.

Working closely with leaders from Parkland Community Health Plan’s (PCHP) team, PCCI developed a predictive model to help reduce the incidence and cost of asthma-related emergency department (ED) visits and hospitalizations among Medicaid-insured low-income Dallas children.

PCHP and PCCI launched the Pediatric Asthma Quality Improvement Program in April 2015. The program was driven by the large number of PCHP members with asthma. Asthma is the most common chronic disease of childhood affecting over 6 million children in the US and resulting in over 140,000 hospitalizations every year.

Asthma disproportionately impacts low-income, urban, Medicaid-insured children compared with privately insured children. Asthma, however, also is an ambulatory-care sensitive condition, meaning that appropriate outpatient care and self-care can prevent unnecessary ED visits and hospitalizations, with subsequent substantial direct and indirect costs savings. The opportunity was ripe to really help disadvantaged children with asthma.

How the predictive model works
Beginning in 2014, PCCI developed a logistic regression model to predict asthma emergency department (ED) visits or hospitalizations within the following three months for children with asthma, using clinical, health services utilization and socio-demographic variables from Medicaid claims data. The risk prediction model classifies every patient as Very-High-, High-, Medium-, or Low-risk for asthma ED visits or hospitalizations and the prediction is updated every month, based on new data input.

Compared to published predictive models, PCCI’s model has a very good predictive accuracy (C-statistic 0.84), is derived from a relatively large and diverse population [3] and is well-evaluated [4]. The PCCI asthma model is continuously evaluated and updated every year, to improve its accuracy and enhance actionable insights that guide clinical and community-based interventions. Deep learning methods have been and additional social determinants of health (SDoH) data have been evaluated to enhance model accuracy. Communitywide data sources have been incorporated to improve and fully assess model impact. Using this model, we were able to predict high risk asthma patients. We have integrated the risk-score into the electronic health record (EPIC) at Parkland as a Best Practice Alert (BPA), to drive timely and streamlined point-of-care interventions. We also generate monthly reports sent to frontline providers and Case Management teams and other non-traditional stakeholders. The monthly reports contained just the right amount of information on patients’ risk profile to drive seamless clinical and cross-organizational workflow integrations and tailored population-level interventions.

The interventions are adaptable: the reports are used, at the providers’ discretion, to either augment or streamline existing interventions or initiate targeted interventions, depending on clinical/community settings, resources, and priorities. The ultimate goals are to reduce unnecessary hospital utilization and cost, increase patient adherence to medication and preventive office visits, and improve overall health care experience. Moreover, we use the risk prediction model to directly engage higher risk patients into a text messaging program for patient education and medication reminders.

Finally, we used patient’s risk-stratification to identify providers caring for the highest risk patients and community sources of high-risk children for enhanced support for program participation and community-based interventions.

Dallas County Community Health Needs Assessment (CHNA) Quality Improvement (QI) Initiative
In 2019, Dallas County performed a Community Health Needs Assessment (CHNA) through which pediatric asthma was identified as a driver of high morbidity among children in the county. In 2020, a communitywide quality improvement (QI) program was launched aiming to improve asthma outcomes for all Dallas County children through data-driven interventions and cross-systems care coordination, following the PCCI Asthma Program model. To support this community-wide initiative, we enhanced our asthma risk prediction model with the addition of electronic health records data, which, together with claims and social determinants of health (SDoH) data, predict asthma risk among Dallas County children with asthma.

The new model retains a good prediction ability and provides additional clinical insights not previously available using claims data only. With the addition of electronic health records data, our new asthma model can be used for all children irrespective of insurance status, thus expanding the benefits of our program to more vulnerable children with asthma. The asthma text messaging program also has been expanded to impact all children with asthma, irrespective of insurance status.

Moreover, community-based services providers in the social and Public Health sectors have been engaged to use PCCI asthma risk reports for community-based interventions beyond the traditional health care system. Community-based organizations and the Dallas County Health and Human Services department now use PCCI risk reports to drive community-based interventions such as home visits and outreach in community gatherings. The Dallas independent school district is also being engaged to use the risk reports for school-based interventions.

PCCI’s asthma risk model and reports are driving cross-organizational workflows and communitywide care coordination across North Texas, to improve health, educational, and quality of life outcomes for children with asthma and their families.

Insightful Community Risk Mapping
Over the past two years, we have added data insights capability to the program using local and regional maps to identify geographical areas with high risk patients and support targeted community outreach. Overlaying asthma risk maps with SDoH maps (down to the block group level, see above) has uncovered discrete neighborhoods with asthma-risk and high social needs that might contribute to poor asthma outcomes, including transportation and childcare needs. These opportunity maps are driving community engagement to improve health, education, and wellbeing of children with asthma and their families.
Results

Since inception, PCCI’s pediatric asthma population health framework has not only reduced unnecessary hospital visits and costs, it has improved the healthcare experience for thousands of pediatric patients and their parents. The updated five-year impact report includes:

• Program expanded to support the communitywide Dallas County CHNA Asthma Quality Improvement initiative
• ~93,000 unique children with asthma risk-stratified to-date across both initiatives (PCHP and CHNA Asthma QI)
• Over 22,000 children with asthma risk-stratified every month and ~45,000 every year, with a rapidly increasing impact
• Over ~1800 high-risk children with asthma impacted by the text messaging program
• 21 large and medium community healthcare provider practices actively engaged, including two large Federally Qualified Health Centers (FQHC) and Parkland’s large network of community-oriented primary care clinics (COPC)
• Non-traditional community services providers engaged, including community-based organizations, Dallas County Health and Human Services community health workers, and Dallas ISD, using risk reports for community-, home-, and school-based interventions
• Dallas Fort Worth Hospital Council Foundation engaged as a source of comprehensive communitywide data to support data-driven interventions
• 30 – 40 percent reduction in asthma-related ED visits
• 50 percent reduction in asthma-related inpatient admissions
• 32 – 50 percent increase in providers prescription of asthma controller medications
• 50 percent drop in annual total asthma cost to PCHP
• Approx. $30 million saved as a result of the risk-driven, multi-stakeholder pediatric asthma framework
• Moreover, the text messaging program has yielded an additional 6-fold drop in asthma-related ED visits among participants vs. non-participants
• Over 85% of participants remain in the text messaging program for more than 12 months and >90% feel empowered to care for asthma as a result of the program

Ongoing Program Enhancements
As we continue this program, we are evaluating the role of emerging deep learning models to improve our risk prediction model performance and explanation. Our original logistic regression model served as the baseline benchmark against which deep learning model results would be compared. We, also, are looking into adding block-level social determinants of health to provide additional actionable insights into patients’ asthma risk profile.

Claims data have strengths and insufficiencies worth highlighting. Claims data consist of billing codes that health care providers and facilities submit to payers. claims data follow a consistent format and use a standard set of pre-established codes that describe specific diagnosis, procedures, medications, as well as billed and paid amounts [5]. Additionally, claims data document nearly all interactions a patient has across all the health care systems. They capture broader information for patients and provide access to larger and more diverse patient cohort. Claims data, however, have a time lag of about 30 to 90 days due to the processing time before they are finally added to the database and become available for analysis. We have begun the process of bringing in additional and timely data sources to enhance or supplement claims data, including electronic health records data and communitywide health and social data, which are progressively improving the timeliness, accuracy, and insights of our asthma risk prediction models and risk reports.

Conclusion
In conclusion, patient education, preventive care, and appropriate use of asthma controller medications are the cornerstone of effective asthma care. Accurate risk prediction of asthma ED visits or hospitalizations, timely provider reports, patient education, and communitywide stakeholder engagement drive the prioritization of evidence-based interventions tailored to the highest risk patients, to efficiently reduce asthma-related ED visits/hospitalizations and associated costs, and improve care experience among children with asthma. By bringing together all the factors from PCCI’s predictive model and applying them to thoughtful and direct interventions, at-risk group of children and their families can experience better outcomes that are beneficial from the health, cost, societal, and consumer experience perspectives. Through our comprehensive approach to whole-person care, , the benefits of PCCI’s risk -driven asthma quality improvement initiatives, which started with one health plan, are now reaching deeper into the North Texas community, bringing quality, coordinated care to vulnerable children where they live, learn, and play.

About the author
Yolande Pengetnze, MD, MS, FAAP, Senior Medical Director, joined PCCI in December 2013 as a Physician Scientist while remaining a Clinical Faculty at the University of Texas South Western (UTSW) School of Medicine and a practicing pediatrician at Children’s Health in Dallas, Texas. Her interests include the use of advanced predictive analytics integrating traditional and novel data sources to improve health outcomes at the individual and population level. She currently leads multiple projects at PCCI, including two population health quality improvement projects in pediatric asthma and preterm birth risk prevention. She received her MD in 1998 from the University of Yaounde in Cameroon, completed a Pediatric Residency training in 2008 at Maimonides Medical Center in New York City, and a Master of Science in Clinical Science at UTSW.

[1] M. Xu, K. G. Tantisira, A. Wu, A. A. Litonjua, J.-h. Chu, B. E. Himes, A. Damask, and S. T. Weiss. Genome wide association study to predict severe asthma exacerbations in children using random forests classifiers. BMC medical genetics, 12(1):90, 2011.

[2] E. Forno, A. Fuhlbrigge, M. E. Soto-Quirós, L. Avila, B. A. Raby, J. Brehm, J. M. Sylvia, S. T. Weiss, and J. C. Celedón. Risk factors and predictive clinical scores for asthma exacerbations in childhood. Chest, 138(5):1156– 1165, 2010.

[3] M. Schatz, E. F. Cook, A. Joshua, and D. Petitti. Risk factors for asthma hospitalizations in a managed care organization: development of a clinical prediction rule. The American journal of managed care, 9(8):538–547, 2003.

[4] A. L. Andrews, A. N. Simpson, W. T. Basco Jr, R. J. Teufel, et al. Asthma medication ratio predicts emergency department visits and hospitalizations in children with asthma. Medicare & Medicaid research review, 3(4), 2013.

[5] W. J and B. A. The benefit of using both claims data and electronic medical record data in health care analysis. Technical report, Optum Insight, 2012.

3 May 2021

National Asthma & Allergy Awareness Month: PCCI’s Pediatric Asthma Efforts Making A Difference With Dallas Children




To support  May being National Asthma & Allergy Awareness Month, please review the exciting programs and innovation PCCI has spearheaded to help children manage asthma:

27 MARCH 2020

IN THE NEWS: HCPLIVE – TEXT MESSAGE PLATFORM IMPROVES ASTHMA OUTCOMES

Yolande Pengetnze, MD, MS, senior medical director at PCCI spoke to HCPLive about a texting program designed to improve outcomes for patients with asthma and pregnant women, and how the technology can be used at other health systems and for other chronic conditions. Click the image below to read the full interview:

15 JANUARY 2020

PCCI POSTER PRESENTATION: PRETERM BIRTH & ASTHMA POSTER FROM IHI SCIENTIFIC SYMPOSIUM

PCCI’s text messaging program on asthma and preterm birth prevention was featured as a poster presentation at December’s Institute for Healthcare Improvement (IHI) Scientific Symposium. The poster program titled “A Novel Evidence-Based Approach to Digital Outreach Improves Patient Engagement and Health Outcomes in Two Distinct Cohorts of Medicaid Patients,” was presented by by PCCI’s Senior Read More »

30 DECEMBER 2019

2019 YEAR IN REVIEW: PEDIATRIC ASTHMA

Among its accomplishments this year, PCCI reported how its predictive modeling helped reduce the harm caused by pediatric asthma. Please click on the image below to see how PCCI’s efforts were applied:

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 Read More »

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 Read More »

17 JULY 2019

DALLAS MEDICAL JOURNAL: PEDIATRIC ASTHMA CONFRONT THE BARRIERS

PCCI’s mission is to support our community’s vulnerable populations, which includes helping children with chronic health issues, such as pediatric asthma. PCCI has been working for several years developing and testing predictive models to identify children at risk for asthma exacerbations. You can now see how this predictive modeling was used to help support pediatric asthma patients Read More »

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.

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

Register your team to receive a complimentary set of “Building Connected Communities of Care” and kick off your Executive Book Club with a consultation from one of our experts.

Sign up to receive email updates on PCCI announcements, advancements in the industry, and more!
Loading