Lesson 8: HMIS Data Quality PDF

LESSON 8 HMIS DATA QUALITY  LEARNING GUIDE TIME LEARNING Lesson 8 : HMIS Data Quality ALLOTMENT RESOURCES Intended Learning Outcomes 1.5 hours Lesson Content:...

LESSON 8 HMIS DATA QUALITY  LEARNING GUIDE TIME LEARNING Lesson 8 : HMIS Data Quality ALLOTMENT RESOURCES Intended Learning Outcomes 1.5 hours Lesson Content: 8.1. discuss data quality, quality assessment, and Lesson 8 development implementation plan; 8.2. explain the importance of data quality tools; and 8.3. differentiate the different techniques used in root cause analysis. Self-Directed Activity Choose one root cause analysis technique. Identify a 2.0 hours problem in your institution and formulate a diagram of your chosen technique. Baseline Concept Understanding 30 minutes Learning Outcomes Assessment 30 minutes  Aspects of Data Quality accuracy completeness DATA QUALITY update status relevance Data quality is the overall utility of a consistency dataset(s) as a function of its ability to be reliability processed easily and analyzed for a appropriate presentation database, data warehouse, or data accessibility analytics system. THE LOT QUALITY ASSESSMENT (LQAS) This is a tool that allows the use of small random samples to distinguish between different groups of data elements (or Lots) with high and low data quality. The Routine Data Quality Assessment Tool (RDQA) is a simplified version of the Data Quality Audit (DQA) which allows programs and projects to verify and assess the quality of their reported data. It aims to strengthen their data management and reporting systems. The objectives are as follows: 1. Rapidly verify the quality of reported data for key indicators at selected sites. 2. Implement corrective measures with action plans for strengthening data management and reporting system and improving data quality. 3. Monitor capacity improvements and performance of data management and reporting system to produce quality data. Table 8.1 Uses of the RDQA Tool Source: RDQA User manual, 2015 DEVELOPMENT IMPLEMENTATION PLAN An Implementation Plan is a project management tool that illustrates how a project is expected to progress at a high level. It is developed through the following key steps (Smartsheet, 2017): Designate team Define Define metrics for Schedule Milestones Allocate Resources member Goals/Objectives success responsibilities DATA QUALITY TOOLS A data quality tool analyzes information and identifies incomplete or incorrect data. Recently, these tools started to focus on Data Quality Management (DQM), which generally integrate profiling, parsing, standardization, cleansing and matching processes. (Goasdue, Nugier, Duquennoy, and Laboisse, 2007) ROOT CAUSE ANALYSIS A root cause analysis is a problem solving method that identifies the root causes of the problems or events instead of simply addressing the obvious symptoms. The aim is to improve the quality of the products by using systematic ways in order to be effective (Bowen, 2011).  TECHNIQUES IN ROOT CAUSE ANALYSIS Failure Fishbone or Mode and Ishikawa or Kepner-Treg RPR Pareto Fault Tree Currently Effects Cause-and- oe Problems Analysis Analysis Reality Tree Analysis effect Technique Diagnos (FMEA) diagrams  KEY POINTS TO REMEMBER ✔ Data quality is the overall utility of a dataset(s) as a function of its ability to be processed easily and analyzed for a database, data warehouse, or data analytics system. ✔ The Lot Quality Assessment (LQAS) is a tool that allows the use of small random samples to distinguish between different groups of data elements (or Lots) with high and low data quality. ✔ The Routine Data Quality Assessment Tool (RDQA) is a simplified version of the Data Quality Audit (DQA) which allows programs and projects to verify and assess the quality of their reported data. ✔ The development of an Implementation Plan is important in ensuring that the communication between those who are involved in the project will not encounter any issues and work will also be delivered on time. ✔ A root cause analysis is a problem solving method aimed at identifying the root causes of the problems or events instead of simply addressing the obvious symptoms. ✔ Techniques in Root cause analysis include Failure Mode and Effects Analysis (FEMA), Pareto Analysis, Fault Tree Analysis, Current Reality Tree (CRT), Fishbone or Ishikawa or Cause-and-Effect Diagrams, Kepner-Tregoe Technique and RPR Problem Diagnosis.