AGENDA. o DATA TO INFORMATION TO KNOWLEDGE TO WISDOM onot ALL DATA IS GOOD DATA. o COMPLIANCE AND DATA

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1 Don t Confuse Data with Information! SUSAN H. FENTON, PHD, RHIA, FAHIMA ASST. DEAN FOR ACADEMIC AFFAIRS, UTHEALTH SCHOOL OF BIOMEDICAL INFORMATICS AT HOUSTON AGENDA o DATA TO INFORMATION TO KNOWLEDGE TO WISDOM onot ALL DATA IS GOOD DATA odefinitions OF BIG AND SMALL DATA o COMPLIANCE AND DATA otypes OF DATA owhat SHOULD YOU DO WITH YOUR DATA? o BENCHMARKING DATA 1

2 Data to Information to Knowledge to Wisdom Best practices for attaining the desired control of diabetes as measured by HbA1C Optimal HbA1C levels for diabetics control Creating a trend line of HbA1C blood results HbA1C results Is the Data good? o Good equals fit for purpose ois there a standard? Ad-hoc Consensus Mandated odo you know (and trust) the source? 2

3 Definitions obig DATA Measured as exabytes: Wikipedia definies1 EB = B=10 18 bytes=1000petabytes= 1billiongigabytes Usually runs over multiple computers and servers using massively parallel software Poor data quality gets lost or simply doesn t matter osmall DATA Everything else Can run on desktops or local servers Data quality DOES matter Compliance Data is (usually) Small Data o Interested in outliers or individual cases Data quality is important Data errors can make a difference o Use entire dataset Trending Identify failure points 3

4 Data Basics o Singular or plural data o Numerical or alphabetical o Categorical or discrete Nominal or ordinal o Continuous Ratio or interval Questions to Ask about Data or Information 1. For which purpose or use was the data originally collected? 2. Are there any data standards related to the data? 3. How was the data collected? 4. What is the quality of the data? 5. How will the data be analyzed to produce information? 6. How can the data or information be presented so that it is meaningful to users? 4

5 Data Quality o Data Quality Management Model Application Collection Warehousing Analysis Data Quality Characteristics oaccuracy o Accessibility o Comprehensiveness o Consistency ocurrency o Definition o Granularity oprecision orelevance o Timeliness 5

6 DATA QUALITY CHARACTERISTIC AND DEFINITION Accuracy The extent to which the data are free of identifiable errors. Accessibility Data items that are easily obtainable and legal to access with strong protections and controls built into the process. Comprehensiveness All required data items are included; ensures that the entire scope of the data is collected with intentional limitations documented. Consistency The extent to which the healthcare data are reliable and the same across applications. Currency The extent to which data are upto-date; a datum value is up-to-date if it is current for a specific point in time, and it is outdated if it was current at a preceding time but incorrect at a later time. HEALTHCARE EXAMPLE The data element gender is completed for all patients and a random check of 500 records performed annually revealed only one demographic data element in conflict with the documentation. All persons with access to the EHR have the ability to search the master patient index and the search function is designed such that the wrong patient is rarely accessed. Providers may decide that recording external cause data is not useful. If so, their data dictionary for the diagnoses data elements would need to include this as an intentional limitation. Within the EHR data such as allergies must be consistently displayed within different applications or screens to prevent confusion. Patient age is generally current when the care is delivered. If age is not reentered the software needs to be have functionality to automatically update the age when appropriate. DATA QUALITY CHARACTERISTIC AND DEFINITION Definition The specific meaning of a healthcare-related data element. HEALTHCARE EXAMPLE Address as a data element label can mean the street address or it can mean the entire address to include city, state, and zip code. Granularity The level of detail at which the attributes and values of healthcare data are defined. Adult weights are usually only recorded in pounds, possibly tenths of a pound. Newborn weights must be recorded in terms of ounces for accuracy. Precision Data values should be strictly stated to support the purpose. Diagnosis Related Group values are carried out to four digits behind the decimal. It would be inaccurate to have the system only use two digits behind the decimal. Relevance The extent to which healthcarerelated data are useful for the purposes for which they were collected. Timeliness Concept of data quality that involves whether the data is up-to-date and available within a useful time frame; timeliness is determined by the manner and context in which the data are being used. Recording a primary diagnosis for hospital inpatients would be irrelevant because coding guidelines mandate collection of the principal diagnosis, which can be entirely different. It would be inappropriate for blood pressure readings from intensive-care unit (ICU) monitors to only be updated each hour. 6

7 Data Quality Assessment & Management Process owho are the data consumers? owhat are the needs of the data consumers? owhat are the required features & quality characteristics? ohow well do our current information products meet the needs & requirements? owhere are the gaps and how important are they? Data Analysis o Understanding the data Data dictionary Methods of collection o Cleaning the data Identify errors Descriptive statistics Categorical data Use of crosstabs Determine correct values or impute If uncorrectable delete the record 7

8 Data Cleaning Continuous Data Data Cleaning Categorical Data 8

9 Data Cleaning - Crosstab Data Analysis o Analyzing the data Goals & objectives Level of analysis of the study Limitations of the data Tools available Analysis needs Use of the results 9

10 Data Analysis Evaluation oreview new data elements from data set merges o Review new derived or computed data elements overification of analysis timeframe and any data subject to change over time o Are statistical analyses correct? o Do counts, sums, averages make sense? Clear Data Presentation owords concise and accurate otables The table should be a logical unit that is self-explanatory and stands on its own. The source of the data in the table should be specified. Headings for rows and columns should be understandable. Blank cells should contain a zero or a dash. Formatting for headings and cell contents should be consistent so that the eye is not confused (Horton 2013, 508) 10

11 Charts and Graphs odistortion The representation of numbers or percentages should be proportional to the quantities represented. oproportion and scale Graphs should emphasize the horizontal and be greater in length than height. A general rule is that the y-axis (height) be three-quarters the x-axis (length) of the graph. oabbreviations Any abbreviations used should be spelled out for clarity. ocolor Color should be used as appropriate to the use of the graph. If the chart is going to be printed will it be printed in black and white or color? otext The font and use of capitalization needs to be considered carefully. The use of all capital letters can sometimes be difficult to read (Horton 2013, 510) Sites to Help with Benchmarking oe/m Code Benchmarking: o 11