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Working title:

Building Capacity of Community Health Centers to Overcome Data Challenges with the Development of an Agile COVID-19 Public Health Registry: A Multi-State Quality Improvement Effort


Authors (in alpha order):

Pedro B Carneiro Hollie Clark Tebitha Mawokomatanda Michael Park Catharine Riley Lisa Romero  Justin Runac Julia Skapik  Raymonde Uy

Audience:

Junction of public health and informatics expertise-- with goal that individuals from each background will yield cross-disciplinary knowledge from the paper.

Intro:(blue star)

  • During the COVID-19 public health emergency, HRSA funded health centers have provided and continue to provide COVID-19 testing, follow-up care, and vaccination to medically underserved populations; health centers are capable of reaching areas disproportionately affected by the pandemic.
  • COVID-19 pandemic revealed health centers were not ready to quickly pivot in a new emerging and critically important domain
  • Infrastructure for public health data is one of the most obvious gaps 
  • Clinical organizations will not be able to rapidly deploy data solutions in a crisis because they appropriately will be focused on crisis operations and care activities
  • All this infrastructure and preparation should be singular, reusable and cumulative
  • CDC, in collaboration with the NACHC, is building health centers’ capacity to respond to the COVID-19 pandemic in multiple states/regions through the development and use of a public health data infrastructure for data aggregation and data quality improvement to support utilization of existing data, systems, and data collection of their patient populations to describe the health burden, disparities, and overall impact of COVID-19.

Methods:(red star)

  • Describe CDC/NACHC project (goal/objectives, multi-state project partners and reach, project approach)
    • Human Centered Design
    • Resusable Infrastructure
    • Cloud-based
    • Internally highly standardized and structured with a staging area for data which cannot or does not need to be fully normalized
  • Build a public health infrastructure for COVID-19 for project partners (data aggregation, quality improvement, and connect partners to Health Information Technology (HIT) innovation opportunities.
  • Using a retrospective cohort design, describe the multi-state project partners readiness, challenges and lessons learned from an assessment of gaps in data, development of data dictionary, monthly pulls of data for quality improvement, data standardization, and data registry to collect patient-level data and monitor key COVID-19 outcomes (e.g., testing, treatment, disease severity, vaccination, risk factors) and health center operations.
  • Describe the outcomes for the data strategy (build a set of initial definitions to support public health evaluation and response; automate data extraction and sending it electronically to a registry using Application Programming Interfaces (API)

Results:(red star)

  • Analyses of data categories
    • Challenges to data capture and use (e.g., lack of data standardization, decentralization of data, poor electronic health information exchange, and inconsistent infrastructure and data reporting requirements for public health).
    • Quality improvement efforts (e.g., monthly data extraction of core metrics, improved the interface and workflow to address gaps in data capture to understand the impact of COVID-19 on populations served by health centers and help manage patient conditions and risk).
  • Presentation of data
    • Tail of unique representations of labs with value sets
    • Distribution across partners-- scatter plot?

Discussion:(blue star)(red star)

  • Pre-project identified challenges
    • Standardization of existing and new COVID content
      • Existing concepts-- fever, vitals, comorbidities, demographics-- the pandemic doesn't solve our missing data data problem but otherwise we can build on this
      • Creating new concepts- C19 IA, CDC, SHIELD/LIVD–these efforts were rapid and critical to using the existing infrastructure to capture basic data: labs, diagnoses
      • New concepts outside of traditional domains: COVID travel risk, exposure activities, masking, PPE, essential worker status
    • EHR support for data standardization and extraction:
      • Labor intensive because of need for manual mapping and validation
      • Worse than expected based on federal requirements-- not all EHRs even can make that basic bar
    • Sometimes complicated by third party vendors 
  • Inter-project challenges
  • Level of effort for partners using pop health vendor-- pros and cons
  • Challenges with EHR partners
    • Difficulty in extracting data elements associated with standard terminologies
      • Data extracts reported as string, or with proprietary EHR vendor codes that are not mapped to reference terminologies
  • Legal and data sharing issues

Conclusions:

  • The process of data extraction and validation is incredibly underrecognized and difficult despite: EHR use, federal regulation, even use of additional tools
  • QI of data is a formal requirement for all clinical QI and public health activities
  • A strong collaboration of all the critical stakeholders is necessary to make any positive impact to move the current "non-system" to a truly Agile learning health system
  • The multi-state project built an infrastructure to standardize, improve, and coordinate data quality and sharing among health centers to describe the health burden, disparities, and impact of COVID-19.
  • The reusable infrastructure, lessons learned, and health information technology tools can be adapted and scaled-up for other health center networks in coordination with public health, hospitals, and community-based organizations to inform local, state, and national COVID-19 response efforts.
  • Infrastructure to enable the activities in the COVID pandemic cannot and should not be built and implemented during a crisis-- it must be intelligently designed, tested and integrated into workflow in advance

Journal

Journal of AMIA (JAMIA) submission guidelines

Workplan


LeadDate DueAction Items
  •  Initial outline

 


  •  Concept Proposal

 


  •  Receive approval/clearance to proceed from CDC 

 

Approved with non-research designation
  •  Detailed outline

 

  •  Julia Skapik add to the calendar invitations for and agendas for C19 with CDC to spend 15 minutes speaking about the Paper - Andrea Price is not on those invitations.
  •  Draft the Introduction/frame the manuscript

 


  •  Pedro B Carneiro and NACHC team will draft the Methods and Results for C19 paper, including any tables or figures by .  






  •  Pedro B Carneiro and NACHC team finalize NACHC sections of C19 Paper draft by  









  •  Lisa will start an Endnote data base for references and maintain it for manuscript.
  •  Pedro B Carneiro and NACHC team send Draft 1 of C19 paper to co-authors for comment by  




  •  Lisa Romero send to pre-clearance for CDC  



  •  Draft 2 



  •  Revisions to create Final



  •  Final



  •  Submission to JAMIA