Health Data Analysis Specialty Track Curriculum Competencies

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1 Health Data Analysis Specialty Track Curriculum Competencies Concepts to be interwoven throughout all levels of the curricula include: CRITICAL THINKING: For example the ability to work independently, use judgment skills effectively, be innovative by thinking outside of the box PERSONAL BRANDING: For example personal accountability, reliability, self-sufficiency Specialty Track Student Learning Outcomes and Curricular Considerations are derived from the CHDA credentialing exam content and are generally at a higher taxonomic level than similar learning outcomes from the associate level curriculum competencies as the intent of the specialty track is to build on the HIM academic foundation. Completion of this specialty track does not alone enable a student to meet the CHDA exam eligibility criteria. However, the content when combined with work experience and other required education (where applicable), should assist in preparing students for the successful completion of the credentialing exam. See current CHDA eligibility requirements for additional information. Student Learning Outcomes Bloom s Curricular Considerations Level Domain I. Data Content, Structure and Standards (Information Governance) DEFINITION: Academic content related to diagnostic and procedural classification and terminologies; health record documentation requirements; characteristics of the healthcare system; data accuracy and integrity; data integration and interoperability; respond to customer data needs; data management policies and procedures; information standards. Subdomain I.C. Data Governance 1. Take part in the development and maintenance of the data architecture and model to provide a foundation for database 4 Relationship between the data and the organization s strategic goals and priorities. design that supports the business needs. Data models (conceptual, logical, and physical) Basic knowledge of various architecture platforms (ie. Oracle, SQL server) Relational database structure (primary key, secondary key) Database language (SQL, XML, etc) 2. Evaluate existing data structures using data tables and field mapping to develop specifications that produce accurate and properly reported data. 5 Standard administrative healthcare data (ie UB-04, CMS form 1500) Classification systems data (ie ICD, CPT, SNOMED-CT, LOINC) 1

2 Conduct Needs analysis Subdomain I.D. Data Management 1. Formulate validation strategies and methods (i.e., system edits, reports, and audits) to ensure accurate and reliable data. 6 Systems testing (integration, load, interface, user acceptance) Industry standards (regulatory requirements) Best practices for auditing (audit guidelines, system audit trails, and audit logs) Communication tools to share outcomes Align outcomes with organizational performance improvement initiatives 2. Facilitate the update and maintenance of tables for organization s information systems in order to ensure the quality and accuracy of the data. Subdomain I.E. Secondary Data Sources 1. Integrate data from internal or external sources in order to provide data for analysis and/or reporting. 2 4 Applicable data standards (ie. ASTM, CDISC, HL7) Source systems (HIS systems, pharmacy, radiology, financial, etc.) Reference classifications/terminology systems and industry data sets requirements (ICD, CPT, UB-04, revenue codes, etc) Classification systems and their history (ie retirement of codes and their allowed reuse with new descriptors) Structure of the data tables Scheduled updates of source system content Design action plans for coordination Industry standard maps between classification systems. 6 Data sources primary/secondary o UHDDS, HEDIS, OASIS Specialized data collection systems (HIS systems, pharmacy, radiology, financial, etc) Registries Relational database structure (primary key, secondary key) Software applications External data reporting requirements: o TJC o CMS

3 o o o CDC State DoH Payers Domain III. Informatics, Analytics and Data Use Definition: Creation and use of Business health intelligence; select, implement, use and manage technology solutions; system and data architecture; interface considerations; information management planning; data modeling; system testing; technology benefit realization; analytics and decision support; data visualization techniques; trend analysis; administrative reports; descriptive, inferential and advanced statistical protocols and analysis; IRB; research; patient-centered health information technologies; health information exchange; data quality Subdomain III.C. Analytics and Decision Support 1. Interpret analytical findings by formulating recommendations for clinical, financial, and operational processes. 2. Ensure results through qualitative and quantitative analyses to confirm findings. 5 Analytics and decision support o Data visualization, dashboard, data capture tools and technologies o Quality standards, processes, and outcome measures o Risk adjustment techniques o Business processes (ie workflow, system limitations, regulatory and payor guidelines) o Industry standard terms of clinical, financial, and operational data 5 Source data content and field attributes Qualitative and quantitative analysis techniques Healthcare operations to improve clinical and financial outcomes 3. Perform routine and ad-hoc reports using internal and external data sources to complete data requests. Subdomain III.D. Health Care Statistics 4 Database programs such as Access or SQL Server Basic understanding of database query syntax (such as SQL) Basic understanding of SAS, or SPSS procedures Data presentation techniques (ie graphic tools, excel) 3

4 1. Analyze health data using appropriate testing methods to generate findings for interpretation. 4 Mean, frequency, percentile, standard deviation Appropriate use of data mining techniques Subdomain III.E. Research Methods 1. Design metrics and criteria to meet the end users needs through 6 Standard healthcare data sets the collection and interpretation of data. Classification systems and clinical vocabularies and nomenclature (ICD, CPT, HCPC, LOINC, SNOMED-CT, NCD, etc.) Basic principles of clinical, financial, and operational data Quality standards and outcome measures Meta-analyses Longitudinal/cross-sectional studies Subdomain VI.F. Strategic and Organizational Management 1. Format information in a concise, user-friendly format by 4 Strategic and organizational management determining target audience needs to support decision processes. Workflow and process monitors Resource allocation Outcomes measures and monitoring Corporate compliance and patient safety Risk assessment Customer satisfaction Internal and external 2. Recommend solutions based on analytical results to improve 5 Information and data strategy methods and techniques business processes or outcomes. Data and information stewardship Critical thinking skills Subdomain VI.I. Project Management 1. Interpret project management methodologies 5 Project management methodologies o PMP Subdomain VI.K. Enterprise Information Management 1. Create uniform definitions of data captured in source systems to 6 Applicable data standards (ie., ASTM, CDISC, HL7) create a reference tool (data dictionary) Reference classification/terminology systems and industry data sets requirements (ICD, CPT, UB-04, SNOMED-CT, LOINC) 4

5 Supporting Body of Knowledge (Pre-requisite or Evidence of Knowledge) Pathophysiology and Pharmacology Anatomy and Physiology Medical Terminology Computer Concepts and Applications 5

6 BLOOM S TAXONOMY REVISED FOR AHIMA CURRICULA MAPPING Taxonomy Category Definition Verbs Level 1 Remember Recall facts, terms, basic concepts of Choose, Define, Find previously learned material 2 Understand Determine meaning and demonstrate Collect, Depict, Describe, Explain, Illustrate, Recognize, Summarize clarity of facts and ideas 3 Apply Use differing methods, techniques and information to acquire knowledge and/or solve problems Adhere to, Apply, Demonstrate, Discover, Educate, Identify, Implement, Model, Organize, Plan, Promote, Protect, Report, Utilize, Validate 4 Analyze Contribute to the examination of information in part or aggregate to identify motives and causes 5 Evaluate Make judgments in support of established criteria and/or standards 6 Create Generate new knowledge through innovation and assimilation of data and information Analyze, Benchmark, Collaborate, Examine, Facilitate, Format, Map, Perform, Take part in, Verify Advocate, Appraise, Assess, Compare, Comply, Contrast, Determine, Differentiate, Engage, Ensure, Evaluate, Interpret, Leverage, Manage, Mitigate, Oversee, Recommend Build, Compile, Conduct, Construct, Create, Design, Develop, Forecast, Formulate, Govern, Integrate, Lead, Master, Propose The layout for the levels and categories was adapted from Lorin W. Anderson and David R. Krathwohl s A Taxonomy For Learning, Teaching, and Assessing, Abridged edition, Allyn and Bacon, Boston, MA

7 CHDA Recommendation Resources: Source: (most current version found at https://www.ahimastore.org/ - under books Products Domains Data Management Data Analysis Reporting BOOKS Horton, M..Calculating and Reporting Healthcare Statistics, Chicago; AHIMA. White, Susan. A Practical Approach to Analyzing Healthcare Data, Second Edition, AHIMA, Chicago, IL Layman, Elizabeth. Health Informatics Research Methods: Principles and Practice, Chicago, IL X X X X E-LEARNING CHDA Exam Preparation Series (AHIMA) OTHER AHIMA Health Data Institute (if available) (http://www.ahima.org/events) X X X x x x 7

8 Editorial Revisions made on Updated the title of the document. Added verbiage to top of the document: Completion of this specialty track does not alone enable a student to meet the CHDA exam eligibility criteria. However, the content when combined with work experience and other required education (where applicable), should assist in preparing students for the successful completion of the credentialing exam. 8

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