AHIMA supports the use of policy to promote the highest level of data quality and integrity possible within healthcare. Stakeholders in the health ecosystem have an obligation to provide the highest quality data possible. Health information (HI) professionals have extensive knowledge and expertise to contribute to developing policies around data quality and integrity as it relates to health information. To make the strides needed to increase the quality of health data, public policy must:
One of the main tenets of data quality and integrity is the completeness of data. Policy should support the development and implementation of consistent data standards to support data content, data mapping, and documentation to improve the collection and use of an individual's health information. This includes ensuring complete representation of a patient’s clinical status in clinical documentation.
High levels of data quality and integrity cannot be attained without ensuring the accuracy of the data. Policy should work to ensure data are the correct values, valid, attached to the correct patient, precise, granular (defined at the correct level of detail), and consistent (reliable across applications). This includes, for example, ensuring that medical coding guidelines and standards support accurate and complete health data. Policy must also ensure
application of consistent standards, across all HIPAA-covered entities, and promote mechanisms for accountability. Accuracy of patient health data also hinges on being able to accurately match patients with their data.
Health data is at its most useful when it is available in a timely manner. Health data, for both patient care and broader public health purposes, should be available for access, exchange, or use in a reasonable amount of time depending on the purpose and context of that data.
As medical technology advances, it must be leveraged to support greater data quality and integrity. Technology that facilitates better documentation and data entry can aid improved data quality and integrity. Security, such as authorizing and authenticating data recipients before exchange, must also be assured.
Policy should recognize the need for workforce development, which may include federal funding or incentives for workforce development specific to health information professionals. This is necessary to maintain data quality and integrity, as technology requires the need for evolving skillsets.
Data quality and integrity requires consideration of privacy and security issues, including the protection of data against risks such as loss or unauthorized access, destruction, use, modification, or disclosures of data by parties not authorized to do so. This includes ensuring that only the minimum necessary information is shared and uses beyond the specific transaction are limited. Policy must also clearly designate and adequately fund oversight and enforcement responsibilities related to such risks.
Clinical documentation is fundamental to every patient encounter. Information must be complete, accurate, and timely to reflect the full scope of services being provided and to ensure that all parties involved in the health ecosystem—from the patient, to the provider, to the payer—are able to make the best decisions with regard to the services provided and the appropriate reimbursement. Along these lines, ensuring data quality and integrity are necessary throughout the lifecycle of patient health data.
Healthcare data has increasingly become electronic over the past decade. During the early 2000s, electronic health record (EHR) adoption was slow and fragmented, often with difficulty integrating between systems within the same hospital. In 2009, the American Recovery and Reinvestment Act (ARRA) was signed into law, and included a portion entitled the Health Information Technology for Economic and Clinical Health (HITECH) Act, which provided more than $30 billion in incentives to expand the adoption and use of EHRs. As the velocity, variety, and volume of electronic health information continues to grow, and as providers and payers continue to move from a fee-for-service to a value and outcome-based reimbursement model, the need for high-quality data will become increasingly important. AHIMA and its members have the expertise to offer insight on this critical issue as policymakers seek to improve data quality
The inclusion of Section 510 in the Labor, Health and Human Services, Education, and Related Agencies (Labor-HHS) section of the federal budget has stifled work around patient identification between the private sector and HHS for more than two decades. Today, lack of widespread operational principles, as well as limitations in processes and technologies, result in inaccurate patient identification. This patient misidentification can include duplicate records and overlaid records, leading to decreased data quality and integrity in patient records.
EHR use has been increasingly associated with clinician burnout, as a result of design inefficiencies that impact provider workflow. These inefficiencies and resulting clinician burnout can lead to reduced data quality and integrity in patient records.
Collection of patient data can vary between, and among, hospitals, clinics, and providers. Data inconsistencies can include legal name versus nicknames, middle name versus middle initial, use of suffixes and hyphens in names, address standards, and number of gender options available. Identity management is a key part of ensuring accuracy and completeness in data quality and integrity. How to handle various sources of patient data can also be a challenge, such as standardizing information that comes from narrative or text reports. Further, the lack of standardization and consistency of clinical information, such as consistent definition of terms and data elements, as well as a lack of consistency in what information is captured (including social determinants of health data), is an ongoing challenge. Varying documentation standards and coding and billing guidelines across payers also hinders data quality and integrity and the ability to analyze information in a consistent, standardized, and meaningful way across different payers.
A 2021 study found that 28 percent of healthcare employees do not believe their company gives them adequate technology training needed to succeed. Lack of training in healthcare technology can directly impact the quality of
patient data being recorded in EHRs and transferred between institutions, as well as with public health reporting. Additionally, more comprehensive education on how to effectively and efficiently produce high-quality clinical documentation must be addressed.
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