Data Governance: Can Healthcare Organizations Afford Not To Govern Their Data?

Data is a critical asset for healthcare organizations. It informs clinical and business processes, is vital to strategic decisions and drives financial results.

Most organizations continue to have silos of data, which have typically grown sporadically as institutions have matured, implementing more systems and adding applications. Many organizations still have no consistent policies that address the quality, use, documentation and other critical aspects of its data; or, if they do, they are out of date or are in conflict across applications. Data governance can rectify these problems.

Data Governance pull quoteThe ultimate goal of a data governance program is to efficiently manage the availability, usability, quality, integrity, and security of the organization's data assets. Yet many healthcare entities consider data governance a low priority.

In our experience, healthcare organizations frequently do not consider the resources already being allocated, often in piecemeal fashion, to activities that may readily be addressed by data governance. For example, many organizations regularly reconcile bad data, consolidate information where there are multiple records per patient or provider, correct multiple spellings for addresses and merge disparate data for analyses.

Data governance provides healthcare organizations with a mechanism to standardize their approach to their data, reducing the frustration and resources they expend on disjointed projects attempting to address the same issues. Any healthcare enterprise contemplating the benefits of committing resources for a data governance program should understand their past and current total resource expenditures on common data issues.

Many healthcare entities without data governance today are habitually expending resources in tackling the following data issues:

Data Quality
There are some common quality considerations regarding usability of data in a healthcare setting. A few of the most familiar include:

1. Provenance: Not knowing the source or transformation history of the data results in not trusting the information derived from the data. Physicians challenge quality reports that do not reflect data they entered into the electronic record. 

2. Missing data: When the quantity of missing data passes a certain threshold, it renders the data inaccurate; many validation checks do not assess missing data. For example, there may be very few body mass index calculations because, while patient weight is regularly captured, patient height is frequently missing from the record. This results in a low BMI quality indicator rate, which has financial implications.

3. Redundancy: Multiple records per patient, more than one person assigned the same ID or multiple IDs per patient or practitioner results in inaccurate data, potential patient harm and expenditures to reconcile the data. Population management becomes challenging as patients are counted more than once or not at all.    

4. Non-standardization: When the structure, format or content of the data is not standardized, it can result in nonsense data or make it difficult to normalize or combine data. For example, age may be expressed as an integer calculated at an annual midpoint in one data set, as an integer calculated at year end in another and expressed as part of a range in a third data set. By standardizing the age integer, data can be normalized and used for accurate reporting.

5. Unsecure databases: Unsecure data warehouses or databases, with little to no control over who can add or write over existing data, affect the accuracy, consistency and user confidence in the trustworthiness of the data. For example, a clinician may add depression assessment scores to the record without linkage to date conducted, referral or follow-up.

Data Security and Compliance
From something as serious as a data breach — where protected patient data is lost, stolen, or exposed — to preparations for an audit, companies spend substantial time and dollars addressing and rectifying data security issues. Not knowing who uses what data and where it is stored makes it nearly impossible to apply appropriate controls to safeguard the data. Other common issues include infrequent password changes or vendor default passwords that have never been changed.

Metadata and Data Documentation
A frequent complaint in healthcare is that data has inconsistent or conflicting definitions. Standardizing data definitions alone represents a huge step forward in many institutions. Furthermore, data documentation may be out of date, in conflict across applications or nonexistent.

Data Architecture
Even with new platforms that offer better data models for integrating, persisting and sharing data, many healthcare organizations continue to experience data integration challenges, particularly with legacy data. Healthcare entities sometimes neglect to align architecture rules with business strategy, assess requirements across all lines of business or ensure adherence to those requirements during the development, implementation and operation of the system.

Extraneous Data
Out-of-date inventories, as well as staff and knowledge overturn, causes many healthcare providers to continue to collect and create data that is no longer used — which, in turn, creates unnecessary confusion and resource expenditures.  

Decision Authority and Accountability
Without data governance, many healthcare businesses experience confusion regarding who has decision making authority and accountability for data-related questions. While there is typically some decision authority regarding data security, it frequently does not extend to all aspects of managing data assets ( e.g., data quality and architecture).This is particularly true in cross-functional and multi-departmental programs where confusion around data accountability often results in either decision paralysis or conflicting decisions. 

Unattended Data Policy
Healthcare entities may already have some data-related policies, usually around data privacy and security, and increasingly around mobile technology. These policies are frequently department or function-specific, do not extend to the entire enterprise and may be in conflict with each other.  Rules may be outdated, misunderstood or ignored across the organization, contributing to data breaches, misuse and quality issues. Critical to the integrity and security of the data are regular policy review, revision, communication, workforce education, measuring, monitoring and managing data and related policies.

Innovation and Business Intelligence
As payment and care models evolve and competition increases, healthcare organizations are increasingly aiming to identify new revenue streams and assess the efficacy of different processes. The need for quick data access and new uses of the data can create conflict with existing data practices. Questions about the integrity of the data tend to dampen enthusiasm and stymie the progress of new initiatives. Trust in and access to the data have a propensity to motivate innovation.

Consider how much time, energy and funding healthcare businesses spend on common data issues and how much could be saved by implementing a data governance program. Multiple data issues are common, and usually one or two take top priority. Data governance provides a model for administering data in a standardized way across an organization, which reduces expenditures on data issues and results in better quality data that meets clinical, business, regulatory and compliance requirements. Healthcare businesses ask whether they can afford to take on data governance; the better question is whether they can afford not to.

Susan Merrill joined Freed Associates in 2012. She has 15 years of experience in scientific epidemiological research and developing strategic data-based solutions for consulting services.

Prior to her position with Freed, Susan was an independent consultant providing data solutions to a variety of healthcare clients. She was also director of analysis for Lumetra, formerly the California Quality Improvement Organization for Medicare. In her career, Susan has worked with medical groups and practices, hospitals, payers, government programs, software companies, and academia.

She holds a PhD and MPH from Yale University and a BS from University of California, Davis.

For additional information, call Freed Associates at 510-525-1853 or visit our website.

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