Data Boot Camp: Part III Effective Data Quality Management. January 14, :00-1:00pm ET

Transcription

1 Data Boot Camp: Part III Effective Data Quality Management January 14, :00-1:00pm ET

2 Who s on the call today Kaye Phillips, Senior Director, CFHI Trevor Strome, CFHI QI & Measurement Coach and Informatics / Process Improvement Lead, Emergency Program, Winnipeg Regional Health Authority and Assistant Professor, Department of Emergency Medicine, University of Manitoba Melanie Rathgeber, CFHI QI & Measurement Faculty and Principal, Merge Consulting

3 Overview This session will cover : Why data quality management policies are important for ensuring high-quality data for healthcare improvement, and how to implement them in your organization; Attributes of effective data quality management policies (including provisions to ensure information security and privacy); The roles and responsibilities of data stewards, including evaluating data quality, identifying issues, and making appropriate recommendations; and How to effectively manage data across an organization, including how data for improvement fits in with existing performance data structures and improvement initiatives. 4

4 Question: Does your organization employ ongoing data quality monitoring and improvement processes? Please type your response in the chat box 5

5 Data Quality Management Cycle Define Improve Validate Manage Deploy Adapted From: 6

6 Manage Improve Define Validate Manage Deploy To ensure the best possible data is available for improvement initiatives: Continuously check data quality & validity Establish baseline data quality levels Monitor data quality trends over time Conduct data quality gap analysis Develop & implement mitigation strategies Establish clear policies and procedures that identify data quality management as an organizational priority Adapted From: 7

7 Data Quality Report - Baseline Data quality report discussed in session 2: useful for baseline summary of data quality 8

8 Data Quality Monitoring & Trend Analysis 9

9 Ongoing Data Quality Monitoring Data quality issues are best managed in a systematic, repeatable manner Avoid repeat work and do-overs Prevent introducing new issues with poor fixes Maximize organizational learning Data quality management becomes part of the data lifecycle Requires discipline and structure to avoid reverting to break-fix mode 10

10 Ongoing Data Quality Monitoring Implement a system for logging, tracking, and managing data quality incidents Choose an approach appropriate for the size/needs of your organization Can range from a simple spreadsheet to a complete incident management system Assign responsibility to an individual or team to monitor tracking system 11

11 Ongoing Data Quality Monitoring For each data quality incident: Specify data quality issue Identify location (i.e., source system, database field) Determine root cause(s) (i.e., user error, improper data validation on input, etc.) Benefits of data quality incident tracking include: Spotting recurrent issues Detecting emerging trends Helping direct appropriate action (i.e., performing a just-do-it fix versus large-scale mitigation effort) 12

12 Data Quality Issue Monitoring (sample) Date Data Quality Issue Source Root Cause (if known) & Notes Reported by Priority Assigned To / Notes 01 September Automated ED visit report contained no data Emergency Department Information System (EDIS) Database ETL process failure Lisa O. HIGH David M (ehealth) to fix ETL code and re-run the procedure. 22 September Duplicate patient survey IDs assigned Patient satisfaction survey New data entry clerk unfamiliar with survey process. Karen S. HIGH Tony D. (Senior data entry specialist) to correct data duplication. 07 October Electronic triage data missing for 25 Emergency patients. Emergency Department Information System (EDIS) EDIS system down between 0800 and 1200 on October 7 th. Pat L. HIGH Note for future data analysis projects that unknown/unavailable data from this period was due to system downtime. 13

13 Gap Analysis Purpose & benefits of a gap analysis is to: Group data quality issues Determine impact and assign priority Identify possible mitigation strategies Assign responsibility to an individual Establish timelines for completion 14

14 Data Quality Gap Analysis (sample) Current State: (Data Quality Issue) Priority & Impact Assessment Future State (Desired Outcome) Mitigation Strategy Assigned To Targeted Date Frequent ETL failures - Data not available for reporting when database load fails HIGH: Visibility into current performance and quality lost to decision makers during blackouts Zero database downtime during peak decision-making hours. Fix/update database ETL procedures. ehealth 15 September Critical data is missing from periods when system downtimes occur because system recovery procedures are not being followed by staff. HIGH: this data is necessary for monitoring clinical quality and performance. No critical data is lost as a result of system downtime occurrences. Standard Work ensure that all staff are aware of established procedures for system recover and data entry when downtimes are over. Emergency Program 19 October 15

15 Reflections 16

16 Question: What are some ways in which your organization improves the quality of its data? Please type your response in the chat box 17

17 Improve Improve Define Validate Manage Deploy Good data is vital to effective decision-making Data quality issues may have negative consequences Reduced/impaired decision-making ability Decreased patient quality and safety Mitigation strategies are those controls, rules and processes that can enable an organization to identify and address data flaws before they cause negative business consequences. 18

18 Data Quality Improvement Strategies Input Processing Output / Analysis Decreasing Preference 19

19 Data Quality Improvement Strategies Data Input Occurs at source-system level Includes data input validation rules, end-user training Data Processing Occurs at database level; during transfer from source system, or prior to analysis. Activities include: correct, cleanse, transform, enrich, de-duplicate Reporting, Analysis, & Statistical Approaches Includes: sorting/grouping, filtering, confidence intervals on charts, data profiling 20

20 Organization-Level Strategies 1. Ensure users understand the importance of high-quality data (and the impact of poor data quality) 2. Improve workflows/processes to promote good data practices 3. Focus on improving your most important data 4. Identify and involve all stakeholders (including end-users, database and system administrators, decision-makers, etc.) Each has an important role to play 5. Initiate a data stewardship program Adapted from: 21

21 Roles of a data steward include: Data Stewardship Working with stakeholders to identify and document data usage and quality requirements. Evaluating data quality, identifying issues, and making appropriate recommendations. Ensuring that any modifications to data storage and management are in line with accepted policies and procedures. Ensuring that data is used properly and that it is accessible. Helping to establish enterprise-wide standards for data quality and usage. Source: Strome, Trevor L. Healthcare Analytics for Quality and Performance Improvement. John Wiley & Sons, October

22 Enabling Data Quality Management for Healthcare Improvement Treat data quality improvement as you would any other improvement initiative Define focused DQ improvement goals Establish governance metrics, and success measures Implement proactive, reactive, and ongoing DQ processes Use data quality monitoring to fix data quality issues in early stages prevent larger scale issues from occurring Use data quality metrics to trigger appropriate action & mitigation strategy 23

23 Data Quality & Healthcare Improvement Always keep in mind that the purpose of collecting data (and improving data quality) is to help us improve quality and safety of care for patients! Ensure that the data you have is good enough for this purpose Pursuing perfect data quality will result in diminishing returns on data improvement efforts Know what the data is going to be used for, and how good the data quality needs to be For example, clinical research versus clinical improvement 24

24 Data Quality and Data Usability For most healthcare improvement initiatives, data quality is NOT good enough when the results of improvement efforts are within the margin of error of your data. 25

25 Data Quality & Healthcare Improvement - Summary High quality data is necessary to inform healthcare improvement practitioners and decision-makers. Data quality must account for performance & improvement goals, processes and workflows, and business rules in addition to the data field definitions and validation rules. Do not let lack of perfect data be an obstacle to using data for improvement. Make best efforts to ensure data is high-quality as possible. Organizational commitment to data quality via policies and procedures help ensure sustainability of efforts. 26

26 Reflections 27

27 Upcoming Webinar February 16th: Better Together Campaign: Spreading Family Presence Policies to Accelerate Healthcare Improvement 28

28 Thank you for joining us! 29