Reaching a high level of trust in your organization’s performance measurement data is dependent on people, processes and technology all working together to ensure the highest levels of data quality possible. Data quality is crucial for properly measuring, managing and improving your organization’s clinical or financial performance…and it’s a lot like preparing a meal.You see, the metrics that your organization monitors throughout the year are dependent on the quality of the data used to compile your results. Like a meal, the quality of the dish is going to depend on the quality of your ingredients. The fresher the ingredients, the greater chance you’ll turn out a great dish that people will want to order again and again. Yet not all healthcare organizations are at the same stage of data quality maturity.
So what kind of data ingredients does your hospital cook with? We break down the five stages of data quality maturity and provide a visual to help you identify where your organization is today.
Stage 1: Cooking from a can
In the earliest stage of data quality maturity, the organization’s quality department will typically only look at their data quality when problems arise. There are no reliable measures to routinely monitor the completeness or accuracy of the data, and there is little to no documentation of data definitions or standards. No one is writing down the recipe! While the organization may be able to fix data quality issues when they arise, they will most likely reoccur without formal processes in place to understand the root cause of their data decay.
Stage 2: Exploring the basics
When a healthcare organization evolves into the second stage of data quality maturity, they are slightly more proactive about monitoring their data. Data element standards and definitions are documented for commonly used terms, and there are basic measures in place to track incomplete or invalid data. Root cause analysis is conducted for simple data quality issues, however, it tends to be limited to the department level rather than an enterprise wide effort.
Stage 3: The family legend
In stage three of data quality maturity, the organization implements a more systematic process to measure, monitor and manage their data quality. They have a comprehensive cookbook where all data definitions and standards are clearly defined and documented across the healthcare enterprise. There are technology solutions in place to help monitor and validate data integrity, along with defined processes for manual inspection when data decay occurs. At this stage, root cause analysis is conducted at the enterprise level and basic data quality results are tracked and monitored within one or more applications within the organization.
Stage 4: test kitchen ENTREPRENEUR
As an organization progresses to a higher level of data quality maturity, they have a well-established and well-managed process for change control, certification of data sources and data exchange standards. Data quality is consistently measured, monitored and managed; allowing the organization to be highly proactive, rather than reactive. Just like a test kitchen for new recipes, these organizations have robust test and reference data sets to evaluate the impact of changing data sources on their measure outputs. When the soup gets salty, there are well managed people, processes and technologies in place to conduct root cause analysis to prevent future data decay. Data quality performance is shared with the measure owners and data stewards and data quality is an essential part of the quality management culture.
Stage 5: michelin three-star restaurant
At the highest level of data quality maturity, the organization is functioning like a Michelin Three-Star Kitchen. They have well governed and broadly adopted policies and procedures in place for managing performance data and data quality across the entire healthcare enterprise. Technology platforms support fully automated data quality surveillance and dashboards and include a meta-data warehouse to manage the data that describes the data. Detailed data elements describe how each data element is used in the organization, with rigorous change control processes in place when IT systems are updated, with data conversions or when new applications are implemented. Data quality measurements, minimum thresholds and benchmarks are communicated to both measure owners and end user stakeholders. Stage 5 organizations operate proactively and achieve the highest levels of trusted performance data possible in the world of ever-changing measure specifications, data taxonomies and information technology environments.
When it comes to performance measurement, Medisolv is your vendor partner for people, process and technology. We are dedicated to helping our clients understand and improve their data quality and create highly reliable, accurate and actionable information for improving clinical outcomes. In addition to providing software that is designed to simplify the quality reporting process, we have clinical experts who can work with you and your team throughout the year to assist with data validation and performance improvement.
Want to become an artisanal data quality chef? Contact us today.
Wednesday, January 16, 2019
1 p.m. ET | 12 p.m. CT | 10 a.m. PT
THE QUEST FOR CAMELOT: BEST PRACTICES FOR IMPROVING THE INTEGRITY OF QUALITY IMPROVEMENT DATA
Improving your data is more important than ever because of value-based payments and public reporting. And maintaining data integrity can be an overwhelming and complicated task.
So, what can you do to improve your quality data and ensure that it’s accurate?
During this session, we’ll discuss how to identify potential gaps and risk points that can occur in an organization’s data stewardship program. We’ll also review best practice strategies to increase the “trust factor” of all your clinical quality measures.
Vicky Mahn-DiNicola, RN, MS, CPHQ
VP Clinical Analytics and Research