Clinical Informatics & Outcomes

Kelly Daley, PT, MBA

Course Description

This course provides an introduction to clinical informatics applied to multiple heath care settings from a rehabilitation perspective. It provides a context of challenges in the contemporary healthcare environment and conceptual frameworks to address these challenges and enhance value at population and patient levels. A model for translation of evidence into practice is used to explore the pivotal nature of clinical data by leveraging the electronic health record (EHR). Participants will compare real examples of collection and utilization of outcome measures and process measures, and learn key concepts to advocate for EHR build of data collection within feasible workflows. The course will differentiate between several healthcare professionals as our non-traditional partners to achieve value initiatives. A foundation is defined for data security, data privacy, and quality assurance for secondary use of EHR data. Participants will design an outcomes report and a process report. Lastly, EHR add-ons such as wearables and apps are demonstrated to add value to clinical programs.

Objectives

At the completion of this course, the participant will be able to:

  • Explain the context of the industry of healthcare in the age of information.
  • Analyze publically-available healthcare population data tools (visualizations) (eg – Dartmouth Atlas; Gapminder).
  • Describe the primary challenges in contemporary healthcare discussed in this course and frameworks to address them — including the quadruple aim and the concept of value to produce outcomes improvement at an acceptable cost.
  • Examine a model of quality improvement (QI) to translate evidence into practice (TRIP), and the pivotal role that rehabilitation data plays in its success for outcomes measurement.
  • Advocate for EHR build in terms of discrete and reportable data collection within feasible and efficient clinical workflows.
  • Differentiate between several healthcare professionals as our non-traditional partners to achieve value initiatives.
  • Apply foundational knowledge of data quality assurance for secondary use of EHR data.
  • Distinguish between outcome measures versus process measures.
  • Design an outcomes report and a process report for the population level and compare them to a model.
  • Propose EHR add-ons which can add value to clinical programs through between-visit patient engagement and population health surveillance.

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