What We Do

Lantana Retreat 2018

eHealth Data and Technology:
Value or Burden?

Too often information is:

Captured, but unavailable for re-use and reporting

Interoperable, but not where needed

Compliant, but not meaningful

All this for way too much time and money spent.

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Our Answer:

We assess your status quo; guide your strategy for maximum ROI; design solutions that work with your investments; use standards that optimize interoperability, measurement, and reporting.

Improving Healthcare:
Transforming Health Information

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  • ASSESS

    How do you chart a path forward that navigates diverse, evolving demands on your data while keeping focus on your mission?

    We analyze your technology and assess your readiness for next steps.

  • STRATEGY

    How do you leverage investments in Health IT to maximize the value of data at the point of care? Which trends in Health IT are priorities?

    We develop a strategy to maximize the value of your health data, technology, and time investments.

  • SOLUTIONS

    How do you implement technology that addresses your needs and follows the changing quality reporting landscape?

    We design solutions that increase efficiency of public and quality reporting.

  • STANDARDS

    How do you collect data for maximum benefit, while minimizing cost and rework?

    We develop and implement standards that create interoperability and support analysis and reporting.

Methods & Tools

Our methods and tools have grown empirically, organically, over a decade, through hundreds of client engagements. They continue to evolve in service to our clients and in support of our Solutions.

Data Analysis Methodology

Our data analysis methodology assesses the feasibility of meeting our client’s analytics and reporting goals. We assess our client’s data and goals simultaneously, identify gaps, and make recommendations that take the path of least resistance to meeting their goals. Our methods maximize the value of the data by recommending the most efficient path to meeting analytics and reporting objectives.

See Solutions & Related Blogs

Data Maturity Model

Lantana’s data element maturity model is a rating system to assess the availability and variability of data in live EHR environments. A high maturity rating indicates high availability and low variability of structure and form while a low maturity rating indicates a lack of availability, high variability in format, or structure, or both. Our analysts use the maturity model to assess the quality and availability of data and to evaluate data gaps in production environments.

See Solutions & Related Blogs

Field-Tested Methods

Our information management tools support our work designing, managing, and validating information for exchange and data quality analysis. These tools are cost-free and open-source with supported/custom versions available:

  • Lantana Trifolia: a suite of tools for designing and publishing CDA templates/implementation guides and FHIR profiles/implementation guides. Trifolia also supports CDA validation and the development of bi-directional CDA/FHIR transforms.
  • Lantana Validator: a free online community resource that can be licensed for local customization and deployment

See Resources, Related Blogs, & More on Trifolia-on-FHIR for more information on these and other tools and utilities.

Case Statements

Government

Lantana Group

Predictable Reporting: 13 sites, 7 EHRs, 65 measures submitted to State Medicaid

A state HIE needed to find the most efficient path to meet quality reporting targets where each provider site had data that matched up differently ...

Improving Hospital Quality and Accountability through Public Reporting

Collecting and analyzing quality of care and payment data is central to the shift from volume to value-based care. Reporting the results publicly is essential ...

Commercial

Lantana Staff

Predictable Reporting: Community Pharmacists as Care Team Members within the Medical Neighborhood

Community Care of North Carolina (CCNC), the primary care case management program for NC Medicaid, found that it needed consistent, structured, and coded data from ...