Articles Archives – PCCI

14 April 2021

PCCI’s Vulnerability Index Shows Lowest COVID-19 Infection Risk Level for Dallas County




DALLAS – Towards the end of March, Parkland Center for Clinical Innovation’s COVID-19 Vulnerability Index has recorded the lowest infection risk since the Vulnerability Index launched in June of last year.

“After the holidays, we had vulnerability index ratings at nearly 200, which meant the COVID-19 virus was running rampant through our community,” said George “Holt” Oliver, MD, Vice President of Clinical Informatics at PCCI. “It is a great relief to see that the highest vulnerability index rating now is only 16.91. This is a triumph for our county’s public health leaders, providers and residents who have made the sacrifices and efforts needed to bend the curve.”

One of the hardest hit ZIP Codes during the past year, 75211, which includes the areas around Cockrell Hill and Oak Cliff, saw its vulnerability risk hit the high of 196.9 in January. This was the highest level any ZIP code in Dallas County reached. By mid-March, its vulnerability rating was 8.74, a dramatic improvement for an area facing some of the most sever socioeconomic issues.

“This is very good news for the residents of the 75211 ZIP code; however, we advise caution going forward,” said Dr. Oliver. “I believe that our new normal will be continued vigilance. To keep COVID-19 from resurging, everyone who can be vaccinated should seek it, and adhere to local health official guidance that includes direction on social distancing and face covering.”

Launched in June 2020, PCCI’s Vulnerability Index identifies communities at risk by examining comorbidity rates, including chronic illnesses such as hypertension, cancer, diabetes and heart disease; areas with a high density of populations over the age of 65; and increased social deprivation such as lack of access to food, medicine, employment and transportation. These factors are combined with dynamic mobility rates and confirmed COVID-19 cases where a vulnerability index value is scaled relative to July 2020’s COVID-19 peak value. The PCCI COVID-19 Vulnerability Index can be found on its COVID-19 Hub for Dallas County at: https://covid-analytics-pccinnovation.hub.arcgis.com/.

Currently, the 75150 ZIP code, at the intersection of Interstate Highway 30 and 635 has the highest COVID-19 risk at 16.91, down from a high of 107.30 in January. The ZIP code 75204, in east downtown Dallas, has the second highest vulnerability level at 15.81, down from a high of 126.5 in January.

PCCI’s forecast of Dallas County reaching COVID-19 herd immunity is still on-track but reaching that threshold is highly dependent residents receiving their vaccinations.

“With vaccinations available to all adults, we need to get in line and get immunized,” said Dr. Steve Miff, PCCI President and CEO. “We don’t want another year to go by where grandparents can’t hug their grandchildren. We have seen how safe and effective the current vaccines are, so it is the responsible thing to do for our friends, families and co-workers to get immunized.”

While always concerning when adverse reactions emerge, the action by the FDA to pause the J&J vaccine is out of “abundance of caution” and it’s a strong signal of how responsive they are to any potential safety concerns. Cerebral venous sinus thrombosis (CVST) with J&J vaccine has been reported in 6 young women (ages 18-48) among 6.8 million doses in the US. To date, Dallas County has administered 61% Pfizer, 35% Moderna, and 4% J&J. The syndrome has been dubbed vaccine-induced immune thrombotic thrombocytopenia.(VITT), based on a similar syndrome after the commonly-used medication heparin abbreviated HITT. The reported rates are much lower than IV Heparin which is used frequently in the hospital. While the risk benefit ratio of continuing to use J& J vaccine in the US COVID-19 vaccination plan may still make sense given the observed case fatality rate of 1.8% of COVID-19, prudence to understand the situation given the FDA emergency use authorization for use is warranted..

The FDA pause for the J&J vaccine will not significantly impact the PCCI initial estimate for Dallas County’s path to herd immunity by June. We were progressing towards herd immunity at a rate of approximately 3% per week, which was ahead of initial predictions. While the allocations for J&J were scheduled to increase and the latest developments will pause those vaccinations likely for days, up to several weeks, we forecast that Dallas county will continue to make progress at 2-2.5% per  week, which maintains the pace for mid-June.

A year in retrospective
With the COVID-19 pandemic ongoing for over a year, PCCI identified the zip codes with the highest average vulnerability from July 2020 through March 2021. These represent areas which have faced the highest risk during the COVID-19 pandemic to date.

Data Sources:
To build Vulnerability Index, PCCI relied on data from Parkland Health & Hospital System, Dallas County Health and Human Services Department, the Dallas-Fort Worth Hospital Council, U.S. Census, and SafeGraph.

About Parkland Center for Clinical Innovation
Parkland Center for Clinical Innovation (PCCI) is an independent, not-for-profit, healthcare intelligence organization affiliated with Parkland Health & Hospital System. PCCI leverages clinical expertise, data science and social determinants of health to address the needs of vulnerable populations. We believe that data, done right, has the power to galvanize communities, inform leaders, and empower people.

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Authors

Steve Miff, PhD., President & CEO of PCCI, George “Holt” Oliver, MD, Vice President of Clinical Informatics at PCCI and  Thomas Roderick, PhD, Senior Director of Data and Applied Sciences at PCCI.

7 April 2021

“BUILDING CONNECTED COMMUNITIES OF CARE” BOOK EXCERPT Case Study – Engaging Patients—Location and Relationships Matter




Following is an excerpt from PCCI’s book, “Building Connected Communities of Care: The Playbook For Streamlining Effective Coordination Between Medical And Community-Based Organizations.” This is a practical how-to guide for clinical, community, and government, population health leaders interested in building connected clinical-community (CCC) services.

This section is from Chapter 6, “Clinical Providers Track.” The purpose of the Clinical Providers Track is to set out the stakeholders and processes required to integrate clinical entities, insights, programs, interventions, strategies, and measurement for the CCC.

PCCI and its partner Healthbox, offers readiness assessments as a service. If you and your organization are interested, go here for more information: https://pccinnovation.org/connected-communities-of-care/.

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Case Study: Engaging Patients—Location and Relationships Matter

As part of our CCC history, PCCI has developed and tested a number of approaches to identifying individuals within the population of vulnerable and under-served Parkland patients who could benefit from screening for health related social determinants, engaging them in the completion of a brief risk assessment and subsequent linkage to available community resources. As with many of the elements of the CCC, this proved to be a learning experience in which initial, more conventional approaches gave way to new and more innovative approaches of engaging this population to optimize goal
attainment.

RECRUITMENT
Much of the initial work began with screening in the outpatient setting. Parkland has 12 Community-Oriented Primary Care (COPC) clinics located throughout Dallas County to serve local residents. Because the COPCs see a large number of patients on a daily basis, many of whom are considered vulnerable and underserved, these COPCs were determined to be a great
location to conduct the social determinant risk assessments. When a patient checked in for a visit, the office staff would provide the patient with a paper-based screening tool to self-administer. Trained community health workers were available in the waiting area to help, if required. Initially we felt like this approach made sense since the large number of COPC patients translated into large numbers of completed screening surveys. However, while there were a large number of initial screenings, the number was very low of patients that agreed to engage with a PCCI community health worker to connect with local community services. Many stated they were not interested or needed to leave the facility for another commitment. Other patients completed the needs assessment but left the COPC before staff members were able to connect with them. Of these, very few responded to follow-up phone outreach and the ones that did were hesitant about referral to community-based services. The team attributed this gap to the lack of personal engagement at the point of initial screening.

As a result of this initial experience, the team made some changes to the screening protocols. Three concurrent workflows focusing on different points of patient encounters were designed and tested. The three new points included: (1) engagement while the individual was in the ED, (2) engagement of individuals that had already left the ED, and (3) engagement of hospitalized patients on the medical/surgical floors of the hospital.

For the direct engagement while the individual was in the ED, licensed social workers conducted initial face-to-face screenings with patients awaiting care. The social workers were provided a list of eligible patients (those with multiple ED visits in the past year) and went room to room to conduct the screenings and determine if the patients were interested in connecting with community resources. Because many of these patient interactions took place while the individual was in the middle of an ED care visit, the PCCI team member was mindful of this and stepped aside, as needed, to ensure they didn’t interrupt the patient’s care. For those individuals that left the ED before screening, the PCCI team placed these individuals’ names and contact numbers on a sheet and later reached out to them by phone to explain the program and ask if they were interested in receiving information on community resources.

Finally, for those individuals undergoing an inpatient stay in the hospital, PCCI personnel obtained census data reports with information about eligible patients and then staff visited these patients in their rooms to conduct one-on-one conversations to implement the screening tool and to determine if the patients were interested in receiving more information about navigation services to community resources.


As shown in Table 6.1, a key learning from this undertaking was that the site matters in conducting the screenings and successfully connecting people to local programs for support. We learned that engaging patients during their inpatient stay was the optimal care setting in which to conduct screenings and then connect those patients to the appropriate community resources.

Establishing trust with patients early in the process was essential, both for completing the initial screening tool and for facilitating connection to community services. During our initial approach, we relied on self-administered screenings that provided little in the way of opportunity to establish a relationship with patients. Our modified workflow allowed our social workers and community health workers to verbally administer the screening tool and provide additional explanations as part of that exchange. This process also made the transition to navigation services virtually seamless and much more
effective. Feedback from patients has also been positive; most indicated that the information received was useful and many said they would share this information with other family members and close friends.

THE SCREENING PROCESS
The PCCI community engagement team consisted of six community health workers and two master’s-level, licensed social workers. Initially, the team consisted entirely of social workers, but our experience taught us that a blended staff model was more cost-effective. PCCI physician leaders coached all team members on how to be flexible and professional when working in the ED, where care moves at a rapid pace. The team needed to take cues from medical staff on where and when to step in to conduct the screenings. Similar trainings were delivered to those staff visiting patients in the hospital.

Over the course of the 6-month pilot, we were also able to identify a number of key elements that increased both the effectiveness and efficiency of the screening process. For example, we learned that it took on average 15 minutes to complete the assessment tool when it was facilitated by a team member but only 10 minutes when self-administered. While the self-administered survey took less time to complete, we found a much higher percentage of incomplete and inaccurate responses, making many of the screens useless. As would be expected, we also found that older patients—those 65 or older—took on average 20 minutes to complete the facilitated screening survey while younger individuals completed it in half the time. The difference was attributable to the amount of questions asked and attendant conversations, which were much more prevalent with older patients. Finally, once we began to work more closely with the patients and they developed a better sense of the purpose of the work, we encountered very few issues with obtaining consent from the patients to share their information with others.

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24 March 2021

COVID-19 fatalities become the leading cause of death in Dallas County one year into the pandemic 




Dallas – With the anniversary of Dallas County’s first COVID-19 death having recently passed, mortalities due to the pandemic has become the leading cause of death among county residents, surpassing heart disease, cancer and strokes in the past year.

According to the Centers for Disease Control and Prevention (CDC), the first death in Dallas County was recorded on March 19, 2020. By March 21, 2021, deaths in Dallas County from COVID-19 stood at 3,763. This surpassed estimated deaths due to heart disease (3,668), cancer (3,356) and strokes (1,015) during that same period.

COVID-19 deaths in Dallas County saw their steepest increases starting in December. On Dec. 21, 2020, deaths due to COVID-19 stood at 1,841, but in the following three months deaths more than doubled, adding 1,922 more casualties.

“This is a sad milestone for Dallas County,” said Vikas Chowdhry, MBA, Chief Analytics and Information Officer at PCCI. “We can see that COVID-19 claimed the most lives following social gatherings and holiday travel beginning with Thanksgiving through Christmas and New Year’s. Starting in December we saw a startling spike of deaths due to COVID-19 that represented more than all of the deaths in the previous months we had experienced during the pandemic. This offers a valuable lesson going forward, that we must remain vigilant to protect ourselves, our families and friends.”

PCCI recently forecast that Dallas County may reach COVID-19 herd immunity by mid-June. However, in order to reach this threshold residents of Dallas County need to continue their efforts to protect themselves from infection. “We are remaining optimistic that we can reach herd immunity by the early summer, but the key is ongoing vigilance, including continued adhering to local health official guidance, social distancing, face covering, and registering for vaccinations as soon as possible,” said Chowdhry.

An animated graphic showing the evolution of the COVID-19 mortality rate in Dallas County can be viewed at https://covid-analytics-pccinnovation.hub.arcgis.com/, PCCI’s COVID-19 Hub for the region. This shows total COVID-19 deaths by day, based on data provided by the New York Times COVID-19 data tracking project. The mortality data includes both confirmed cases, based on laboratory testing and probable cases, based on specific criteria for symptom and exposure. This is per guidance form the Council of State and Territorial Epidemiologists.*

To help protect Dallas County residents, PCCI recently launched the MyPCI App, a web-based program to help inform the residents of Dallas County to their individual risks. The MyPCI App, free to register and use, is a secure, cloud-based tool that doesn’t require personal health information and doesn’t track an individual’s mobile phone data. Instead, it is a sophisticated machine learning algorithm, geomapping and hot-spotting technology that uses daily updated data from the Dallas County Health and Human Services (DCHHS) on confirmed positive COVID-19 cases and the population density in a given neighborhood. Based on density and distances to those nearby who are infected, the MyPCI App generates a dynamic personal risk score.

To use the MyPCI App, go to, https://pccinnovation.org/mypci/, click on the link and register (Using code: GP-7xI6QT). Registration includes a request for individual location information that will be used only for generating a risk assessment, never shared. Once registered, simply login daily and a COVID-19 personal risk level score will be provided along with information to help individuals make informed decisions about how to manage their risk.

About Parkland Center for Clinical Innovation

Parkland Center for Clinical Innovation (PCCI) is an independent, not-for-profit, healthcare intelligence organization affiliated with Parkland Health & Hospital System. PCCI leverages clinical expertise, data science and social determinants of health to address the needs of vulnerable populations. We believe that data, done right, has the power to galvanize communities, inform leaders, and empower people.

 

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*The tallies reported here include probable and confirmed cases and deaths. Confirmed cases and deaths, which are widely considered to be an undercount of the true toll, are counts of individuals whose coronavirus infections were confirmed by a molecular laboratory test. Probable cases and deaths count individuals who meet criteria for other types of testing, symptoms and exposure, as developed by national and local governments.

10 March 2021

PCCI CEO Steve Miff’s Statement on Changing Texas COVID-19 Mask Policies




27 January 2020

Steve Miff Webcast on Artificial Intelligence for Social Determinants of Health




Recently, PCCI’s CEO & President, Steve Miff participated on a webcast hosted by the University of South Florida for the Florida Medicaid Drug Therapy Management Program for Behavioral Health. His presentation “Artificial Intelligence for SDOH” discussed how social determinants of health and AI can work together to help improve care for under-served populations. Click on the image below to see the video webcast:

 

 

 

21 January 2020

Get Your COPY OF THE BOOK THAT WILL HELP BUILD SUSTAINABLE CONNECTED COMMUNITIES OF CARE




Available now is a new book from PCCI: “Building Connected Communities of Care: The Playbook For Streamlining Effective Coordination Between Medical And Community-Based Organizations.” This is practical how-to guide for clinical, community, and government leaders interested in building connected clinical-community services.

The book shows how to facilitate cross-sector care coordination; enable community care partners to better provide targeted services; reduce duplication of services across partnering organizations and help to bridge service gaps in the currently fragmented system.

Published by HIMSS, the book will be available on March 9. Reserve your copy today at: HIMSS publishing: https://lnkd.in/eiB2Jq7 opens in a new window Or Amazon.com opens in a new window: https://lnkd.in/eBKH9h7 opens in a new window

 

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:

 

26 August 2019

VIDEO: PCCI’s Women in Data Science & Technology Internship Delivered Immersive Experience




A video from Parkland Center for Clinical Innovation (PCCI) highlights its Women in Data Science and Technology Summer Internship program, with members of the program sharing their valuable experiences.

PCCI’s 2019 summer intern program is made up of area students from Dallas Independent School District high schools, SMU’s Statistics Department as well as students from the University of Texas at Dallas and Creighton University.

This internship program has become one of the most prestigious internship programs in North Texas with a mission to expand opportunities for women in an industry that significantly lacks gender diversity.

 

 

 

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.

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.

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.

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