Data Governance

Does your New Year’s resolution include a renewed focus on data integrity? Hopefully your answer is a resounding yes. HIM professionals ensure that health information is accurate, reliable, timely, up-to-date and consistent. The ability to do so has always been our strength. HIM professionals also educate, mentor and inspire each employee to take ownership of the data and understand how his or her actions not only affect the data, but the patient. It is important for us to become data stewards throughout 2014 and beyond as the industry continues to implement EHRs, exchange health information and begin the use of ICD-10.

Embark on the new frontier.
There is no doubt that data is healthcare’s greatest asset. Effectively managing that data improves data quality for research, predictive analytics, patient care and coding/reimbursement. It also reduces data redundancy and enables interoperable exchange.

Information governance is one of AHIMA’s top five strategic initiatives for 2014-2017. AHIMA uses Gartner’s definition of information governance: “the specification of decision rights and an accountability framework to ensure appropriate behavior in the valuation, creation, storage, use, archiving and deletion of information. It includes the processes, roles and policies, standards and metrics that ensure the effective and efficient use of information in enabling an organization to achieve its goals.”

The association encourages its members to embrace the data revolution and become key industry resources for “closing the gap on needed standards to ensure the integrity of quality health information as the industry continues to migrate towards an increasingly electronic environment.” It also promotes HIM involvement to “enable effective and secure sharing of information, give domestic and global healthcare leadership ready access to real-time decision support information and help ensure full benefit realization and return on investment for EHR solutions.”

To do so, professionals must thread HIM core principals throughout their organizations; including core principals of data integrity. Furthermore, data integrity must be implemented at the front end-not retrospectively by the HIM department. Executive buy-in is crucial.

When advocating for data governance, it may be helpful for HIM professionals to consider the organization’s immediate needs. For example, inadequate staffing may be one challenge. Data governance can help organizations identify and streamline redundancies (e.g., core measure data input across multiple registries) that can lead to greater staff efficiency. Proving return on investment for the EHR may also be problematic. Data governance can help organizations identify opportunities to use and repurpose data collected in the EHR, thus increasing the value of the technology.

Create an enterprise data quality steering committee.
The first step toward ensuring an organization-wide commitment to data integrity is to establish a steering committee that includes the CIO, all data administrators and representatives from HIM, patient access, the legal department and risk management.

Data administrators are particularly important because they oversee the data that is entered into their respective systems. This data is part of the overall legal health record. Do these administrators and their departments understand how the legal record is defined? Is there a process to communicate any changes made in source systems back to the HIM department? What is the process for inputting data into these systems?

The enterprise data quality steering committee should perform the following tasks:

• Create, review and validation the organization’s data dictionary and any data maps.
• Review documentation for errors (e.g., errors promulgated by copy and paste techniques in the EHR).
• Create policies and procedures that promote data integrity.
• Devise strategies for user training and ongoing record integrity best practices.

Create a data dictionary.
A data dictionary provides a descriptive list of names, definitions, and attributes of data elements that an information system or database captures. The dictionary is a crucial tool for data sharing, exchange and integration. HIM professionals should drive the effort to create this dictionary as well as ensure a process for modifications. Consider the following key elements of a data dictionary:

• Naming conventions. Different systems may use different conventions for common data elements. This can wreak havoc when comparing data across those systems. For example, the date of a patient’s admission may be ‘date of admission’ in the EHR, ‘admit date’ in the fetal monitoring system, and ‘admission date’ in the cardiology system.
• Definitions. Different systems may also use different definitions for common data elements. For example, the patient access module may define date of admission as the date on which an inpatient or day surgery visit occurs. The trauma registry system may define it as the date on which the trauma patient enters the operating room.
• Field length. Field lengths for the same data element may vary depending on the system. For example, the patient management module may include 16 characters for a medical record number while the cancer registry may only include 13 characters.Element values. Element values may also vary depending on the system. For example, the patient access module may capture gender as M, F, or U while the peripheral vascular lab database may capture it as Male, Female, or Other.
• Structure vs. Unstructured. Variances between structured and unstructured data (free text) must also be identified within the data dictionary. Structured data is defined (i.e. letters/numbers with specified values) while unstructured has no length of defined characters/numbers required).

For more information about how to create a data dictionary (as well as a sample dictionary), download AHIMA’s Health Data Analysis Toolkit.

Create a data map.
Data mapping allows organizations to link or associate data captured in one format or system with data captured in another format or system. Data mapping not only transfers data from one system to another, but it often transforms the data, necessitating the need for strict HIM oversight. Mapping between terminologies and classifications is common (e.g., mapping from ICD-9 to ICD-10). HIM professionals must ensure that data maps support data integrity. In particular, HIM can help to answer these questions:

• Does the data have the same definition historically and going forward?
• Do users and creators of the data understand its construct?
• How will data mapping inaccuracies be identified and resolved?

For more information about how to create a data map, download AHIMA’s practice brief, “Data Mapping Best Practices.”

Reduce duplicates in the master patient index (MPI).
Duplicates in the MPI can cause massive problems for data integrity. The number of duplicates should be no more than 2 percentof all entries in the MPI. Consider the following to remedy or prevent duplicate errors:

• Use a multi-factor ID lookup when searching for a patient simultaneously from the beginning of the patient search. The best practice is to always use at least two-the first and last name count as one. For example, use all four of the following pieces of information: first name, last name, date of birth and Social Security Number.
• Monitor duplicate reports. Do you know how often staff corrects duplicates or overrides prompts to further research a patient’s identity before completing the registration process?
• Require patient access staff members to fix overlaps and overlays concurrently-not retrospectively after the patient is discharged. For example, merge medical record numbers immediately for patients who have more than one number in the MPI or across more than one database (an overlap). Also correct overlays whenever they are identified (patient’s record data is overwritten with data from another patient’s record).

Address copy and paste documentation.
Copy and paste documentation has been a hot topic, given the OIG’s allusion to it in its 2013 Work Plan. The practice of inappropriately copying and pasting information can jeopardize data integrity and cause major problems for providers who then make decisions based on potentially erroneous information. Regulate this problem by creating a copy and paste policy that outlines when providers can-and cannot-use this functionality. Provide education to all users about the potential compliance and patient care concerns related to the copy and paste functionality. How will your organizations monitor the problem going forward?

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Transitioning to an EHR

Best practices for a smooth transition.

Ensure a consistent process for amendments.
Medical record amendments are a routine part of documentation and record-keeping. Thus, it is important to ensure a consistent process for both so that data is easily understood by all parties. Create an amendment policy specific to the EHR, and ensure that all providers understand the steps they must take to insert any changes into the record.

Ensure that vendors support data integrity.
When selecting a technology vendor, consider how that vendor can enable-or potentially complicate-data integrity. For example, when implementing speech recognition, consider the following:

• What is the accuracy rate? How can we monitor this rate proactively?
• Does the software flag an audit, if necessary? How are audits routed to transcriptionists?

Consider the following questions when implementing computer-assisted coding (CAC):

• What is the accuracy rate? How can we monitor this rate proactively, particularly once ICD-10 goes into effect?
• When and how often will coders edit the codes that the CAC software assigns? In what circumstances?

Consider the following questions when implementing an EHR:

• Do EHR templates capture the appropriate information? Are templates able to accommodate ICD-10?
• Does the EHR allow copy and paste documentation? Can the functionality be monitored? Does the EHR flag or somehow identify information that has been copied and pasted? If so, how?

Move up the career ladder.
HIM professionals are increasingly moving into data-centric roles such as data integrity analyst, mapping specialist, informaticist researcher, project manager, research and development scientist, or even chief information officer. If you are interested in any of these roles, refer to AHIMA’s Career Map to determine job descriptions, training and educational requirements, average salaries and transitional pathways.

Each of these new and emerging roles capitalizes on HIM’s strengths to identify and promote clear and valid definitions and rules, resolve data integrity issues and provide training for end users to promote best practices for data collection and use. HIM professionals have-and always will be-the gatekeepers of healthcare data. It is time to embrace this role and showcase our knowledge.

Rita Bowen is senior vice president of HIM and privacy officer at HealthPort. She can be reached at Alisha R. Smith is the health information management educator for HealthPort.

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