Governance Should Lead the Healthcare Data Dance
Originally published abril 20, 2009
Data governance and data stewardship programs are making progress within many healthcare organizations, but often practitioners are unable to explain how governance and stewardship are working together within their organization. To avoid conflicting governance and stewardship paths, a clear, concise data governance model is recommended.
The Legality of GovernanceThe healthcare industry has always been a leader in managing electronic data, especially patient identifiers, and we’ve grown accustomed to the discussions and media coverage about how patient care has been improved as a result. In fact, AHIMA’s November ’08 Practice Brief titled “Health Data Access, Use and Control” notes the importance of data stewardship and states, “We should approach the application of stewardship first by recognizing that healthcare is becoming increasingly patient-centered.” Given these factors, it’s understandable that some might question the need for data governance over stewardship. However, a strong overarching data governance strategy helps organizations function more efficiently and use data to make knowledge-based decisions for both patients and providers. The only way to truly improve patient care, save lives and avoid unnecessary litigation is to purposefully create data governance strategies, then wrap data stewardship policies around them.
In today’s environment healthcare organizations are working with many different forms of a patient record: electronic, paper, images, etc. As the industry moves to electronic health records and cross-organizational sharing of health information, it is becoming critical for healthcare organizations to clearly define what makes up a legal medical record. This is one reason that data governance is becoming a key issue. Organizations potentially put themselves in future legal jeopardy if they do not look at why patient information is being collected, and put data governance processes in place that define the components of a legal medical record and how they will be governed.
The importance of this need is highlighted even more by the growth of regional and state health exchanges. As these multi-stakeholder groups have emerged, data about patient identifiers are being shared across organizations. Healthcare organizations are no longer playing within the same legal environment they were when data was contained within their own four walls. Data governance programs that clearly articulate why data is being used and, in this case, shared between groups help protect healthcare organizations from some of the significant legal implications that they might experience without them.
In summary, a clear definition of the organization and its data needs, combined with the purpose for sharing health information, are the drivers for data. Data governance and data stewardship processes must take these needs into consideration and ensure that data is fit for the intended purpose.
Data Governance’s Impact on Quality and ConsolidationA solid, well thought out data governance strategy does more than just help prevent legal issues. Data governance also provides benefits to the decades-old issues of data quality and provider consolidation.
Organizations that actively create data governance programs around the example of patient identifiers, and implement data stewardship policies and procedures to support them, are best equipped to prevent data errors and to create accurate and complete data, resulting in improved data quality. The best place for errors to be found is at the point of registration, so they can be resolved before a patient receives treatment. Consider the implications for an asthmatic who is brought to the emergency room and is unable to verbalize that she is allergic to the traditional treatment of epinephrine. If this small but critical notation is not clearly available in her medical record, because of identity mistakes, the wrong treatment plan might be administered and could jeopardize her life.
Data quality problems are usually identified after a patient has been discharged, which is too late to positively impact the current episode of care. Lagging identification problems can also impact pay for performance initiatives and impede efficient transitions when healthcare organizations merge or are acquired, in addition to compromising patient care. By having a data governance plan in place that clearly articulates accurate patient identification policies and procedures, complementary technology and a solid data stewardship model, organizations can save repetitive work for all parties involved.
The potential for data problems has continued to grow over the past decade, as the number of mergers and acquisitions have significantly increased and resulted in the need to consolidate massive amounts of patient records. Unfortunately data governance plans are not often part of the transition process. Executive teams need to more clearly articulate why and how data is brought together, how it is important to the organization, and which data programs should be deployed to achieve high-level governance strategies that meet corporate objectives, legal requirements and other business goals. These efforts will then enable data stewards to implement programs and procedures that comply with the governance policies and ensure an efficient transition process.
Implementing Data Governance IncrementallyWhat is the most efficient way for a healthcare organization to begin implementing data governance policies? The first step is securing sponsorship from a multi-stakeholder group of executives. Gaining widespread buy in can be difficult, but is necessary to ensure the program’s success. Data governance cannot be driven just by IT, registration or the clinical departments. Its impact affects every aspect of a healthcare organization and must be embraced enterprise-wide.
Since executive time is limited, one way to help achieve support is to leverage an, incremental approach when implementing data governance. Healthcare executives are already used to incremental approaches to data initiatives. Early data practitioners often took incremental approaches to data quality initiatives and data stewardship programs that improved patient care.
Instead of trying to solve the entire data governance problem at once, executive sponsors should focus on one set of data and implement the process in smaller pieces. Ideally the first data set selected should be the one that, once implemented, will deliver the greatest value to the organization, such as patient or provider data. Then the organization should create data governance goals for that data set and begin implementation.
Once organizations understand the roles of governance and stewardship, have achieved executive buy in, and determined which data set to tackle first, they then need to select the data model structure that best suits their specific needs. There are essentially three types: centralized, decentralized and hybrid. Each data model has its own merits. To determine which structure is best for them, organizations should consider their current and future needs, including how their data may grow and evolve with time. Let’s explore the patient identification example and the models that are commonly used with it.
A centralized data stewardship model has a dedicated team that is ultimately responsible for ensuring that the organization’s data quality is at an acceptable level. For example, this team manages how patient data located in multiple files within numerous source systems in an enterprise are handled, and establishes thresholds that determine whether data is automatically linked or requires human examination. A corporate-wide structure and the desire to use data at a corporate level are key drivers for selecting a centralized data model structure. Large integrated delivery networks that already have electronic health records and a department that is responsible for data issues are an ideal candidate for this model.
Decentralized data structures enable each organization or department to handle its own specific data quality issues. Some might be concerned that, with a decentralized approach, there is not an individual specifically assigned to handle cross-source identification of the same patient. However, in a decentralized model, these linkages are usually managed automatically as an IT function, and are typically handled within a single threshold model. This approach is best suited for organizations that are smaller and that have true autonomy of facilities.
A hybrid model enables individual organizations or departments to work with three of the four data quality issues (duplicates, overlays and review identifier tests) themselves, but also has a person or a group in place that handles issues with cross-source groups for the entire organization. A hybrid model can successfully be used, for example, by a hospital that links multiple physician offices.
Once the data model is selected, the implementation on the first data set can begin and be completed within a matter of months. With this approach, data teams are able to learn from and adapt their approach with each project segment, take time off in between increments to focus on other pressing business goals, and then come back together to address another data domain, while making steady progress over time. As governance strategies mature, organizations should keep in mind that supporting stewardship models may need to evolve to continue to add value over time,
The Case for Data Governance in HealthcareData is growing at healthcare organizations around the country and a large number of drivers are pushing the need for data governance and data stewardship. Healthcare organizations that want to ensure that legal, data quality and consolidation initiatives are met must ensure that data governance strategies lead data stewardship tactics. Although every organization has a unique approach to data initiatives, many commonalities exist: for organizations to be truly successful, they must embrace incremental data governance practices that allow for quick returns and should evaluate various data model structures to find a custom fit that matches their individual business needs. When organizations take to the data quality dance floor, if data governance plans lead the data stewardship process there will be impressive results.
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