Hm306 week 1 ppt 1
- 1. © 2016© 2016 A Practical Approach to Analyzing Healthcare Data Chapter 1 – Introduction to Data Analysis
- 2. © 2016 Data Analysis • Healthcare is a data driven business • Data collected – Diagnostic tests – Services provided – Costs and payment – Diagnosis and procedure codes • What is data analysis? – Task of transforming, summarizing or modeling data to allow the end user to make meaningful conclusions Information Data Data Data
- 3. © 2016 Primary vs Secondary Analysis • Primary data analysis is the use of data for its primary purpose – Example: Billing and claims data’s primary use it to determine services rendered and payment to from a patient or third party payer – Performing an analysis of typical payment received from a payer for emergency visits is a primary use • Secondary data analysis is the use of data beyond its primary purpose – Example: ICD-9 diagnosis codes are assigned to a patient to record diseases present or discovered during an encounter – Using a profile of the most common ICD-9 diagnosis categories for the purposes of determining the patient load by service line is a secondary use. – Be aware of primary use and evaluate the secondary use is valid and reliable
- 4. © 2016 Types of Statistical Analysis • Descriptive statistics – Characterizes the distribution of the data – Estimates the center or ‘typical’ value – Measures the spread or variation in the data • Inferential statistics – Using sample data to make conclusions or decisions regarding a population – Not practical to observe the entire population – Often accompanied with a probability of making an incorrect decision based on the sample
- 5. © 2016 Structured vs Unstructured Data • Structured data – AKA Discrete data – Data stored in fields that may be delineated – Values can be listed and validated – Examples • Patient age • CPT code • Laboratory test values • Unstructured data – Free form text captured in narrative form – May be stored in a database field, but the content in not limited to values of a variable – Examples • Progress notes in an EHR • Comments in a patient satisfaction survey • Radiologist’s report of an x-ray result
- 6. © 2016 Qualitative Data • Qualitative data – Describes observations about a subject – Typically free text or comments – May be recoded or placed into categories for analysis – Example: • A nurse describes a patient as having pale skin tone. • Survey question: What do you like most about this course? • Data scales typically used for recoding qualitative data: – Nominal - categories without a natural order • Diagnosis codes • Clinical units • Colors – Ordinal – categories with a natural order • Patient satisfaction surveys • Patient severity scores • Evaluation and management code levels
- 7. © 2016 Quantitative Data • Quantitative data – Naturally numeric – May be categorical (ordinal or nominal) • Data scales found in quantitative analysis – Interval – numeric values where the distance between two values has meaning, but there is no true zero and the interpretation is not preserved when multiplying/dividing • Temperature • Dates – Ratio – numeric values where zero has meaning and multiplying/dividing values has meaning • Length of stay • Age • Weight
- 8. © 2016 Variable Scales/Data Type
- 9. © 2016 Overview of Data Type and Statistics
- 10. © 2016 Inferential Statistics - CMS
- 11. © 2016 Exploratory Data Analysis and Data Mining • Exploratory Data Analysis (EDA) – Used to uncover patterns in data – Typically a secondary use of data – Primarily graphical analysis (plots, trends, etc.) • Data Mining – Also looking for patterns in data – Adds in descriptive statistics and more formal statistical techniques – May be used for benchmarking and determining high/lower performers
- 12. © 2016 Predictive Modeling • Historical data is used to build models to determine most likely outcome in future • Data mining is used to identify the potentially best predictors • Maybe a simple function (linear regression) or more involved models (neural networks) • Examples – Used by CMS for pre-payment reviews to fight fraud – Used by credit card companies to prevent fraud – Used by providers to identify missed charges
- 13. © 2016 Data Analyst Skills • Must be able to combine: – Content knowledge (coded data, healthcare business process, etc.) – Understanding of the strengths and weaknesses of various data elements – Data acquisition skills through querying databases or effectively writing specifications for queries – Ability to identify the appropriate statistical technique to apply – Familiarity with analytic software to produce the required output – Present the analysis to the end user so that it may be the basis for business decisions
- 14. © 2016 Opportunities for HIM Professionals • HIM Professionals are uniquely positioned to: – Understand data structures and coding systems – Understand available data and methods for integration – Can communicate with both finance and IT staff – Act as a business analyst—far more valuable than a pure data analyst
- 15. © 2016 Entry Level Health Data Analyst Responsibilities • Working with data – Identify, analyze, and interpret trends or patterns in complex data sets – In collaboration with others, interpret data and develop recommendations on the basis of findings – Perform basic statistical analyses for projects and reports • Reporting Results – Develop graphs, reports, and presentations of project results, trends, data mining – Create and present quality dashboards – Generate routine and ad hoc reports
- 16. © 2016 Mid-level Health Data Analyst Responsibilities • Work collaboratively – with data and reporting – the database administrator to help produce effective production management – utilization management reports in support of performance management related to utilization, cost, and risk with the various health plan data – monitor data integrity and quality of reports on a monthly basis – in monitoring financial performance in each health plan • Develop and maintain – claims audit reporting and processes – contract models in support of contract negotiations with health plans • Develop, implement, and enhance evaluation and measurement models for the quality, data and reporting, and data warehouse department programs, projects, and initiatives for maximum effectiveness • Act as a business analyst – Recommend improvements to processes, programs, and initiatives by using analytical skills and a variety of reporting tools – Determine the most appropriate approach for internal and external report design, production, and distribution, specific to the relevant audience
- 17. © 2016 Senior-level Health Data Analyst Responsibilities • Understand and address the information needs of governance, leadership, and staff to support continuous improvement of patient care processes and outcomes • Lead and manage efforts to enhance the strategic use of data and analytic tools to improve clinical care processes and outcomes continuously • Work to ensure the dissemination of accurate, reliable, timely, accessible, actionable information (data analysis) to help leaders and staff actively identify and address opportunities to improve patient care and related processes • Work actively with information technology to select and develop tools to enable facility governance and leadership to monitor the progress of quality, patient safety, service, and related metrics continuously throughout the system
- 18. © 2016 Senior-level Health Data Analyst Responsibilities • Engage and collaborate with information technology and senior leadership to create and maintain: – a succinct report (e.g., dashboard), – a balanced set of system assessment measures, that conveys status and direction of key system-wide quality and patient safety initiatives for the trustee quality and safety committee and senior management; – present this information regularly to the quality and safety committee of the board to ensure understanding of information contained therein • Actively support the efforts of divisions, departments, programs, and clinical units to identify, obtain, and actively use quantitative information needed to support clinical quality monitoring and improvement activities • Function as an advisor and technical resource regarding the use of data in clinical quality improvement activities • Lead analysis of outcomes and resource utilization for specific patient populations as necessary • Lead efforts to implement state-of-the-art quality improvement analytical tools (i.e., statistical process control) • Play an active role, including leadership, where appropriate, on teams addressing system-wide clinical quality improvement opportunities