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These features address traditional well-known requirements for documentation principles while supporting expansive new technologies. Use of these features without appropriate management and guidelines, however, may create information integrity concerns such as invalid auto-population of data fields and manufactured documentation aimed to enhance expected reimbursement.
Processes must be in place to ensure the documentation for the health information used in care, research, and health management is valid, accurate, complete, trustworthy, and timely. There are a number of existing rules and regulations on documentation principles and guidelines that primarily address documentation authorship principles, auditing, and forms development in a paper health record.
New guidelines are being sought by the healthcare industry that ensure and preserve documentation integrity in an age of electronic exchange and changes in the legal evidentiary requirements for electronic business and health records.
With the continued advancement of electronic health records EHRsthere is increasing concern that a potential loss of documentation integrity could lead to compromised patient care, care coordination, and quality reporting and research as well as fraud and abuse.
This practice brief provides guidance for maintaining documentation integrity while using automated EHR functions. Ensuring Documentation Integrity Documentation integrity involves the accuracy of the complete health record. It encompasses information governance, patient identification, authorship validation, amendments and record corrections as well as auditing the record for documentation validity when submitting reimbursement claims.
EHRs have customizable documentation applications that allow the use of templates and smart phrases to assist with documentation.
Unless these tools are used appropriately, however, the integrity of the data may be questioned and the information deemed inaccurate—or possibly even perceived as fraudulent activity. Established policies and procedures such as audit functions must be in place to ensure compliant billing.
The provider must understand the necessity of reviewing and editing all defaulted data to ensure that only patient-specific data for that visit is recorded, while all other irrelevant data pulled in by the default template is removed.
For example, the automatic generation of common negative findings within a review of systems for each body area or organ system may result in a higher level of service delivered, unless the provider documents any pertinent positive results and deletes the incorrect auto-generated entries.
Appendix Bavailable in the online version of this practice brief in the AHIMA Body of Knowledge, illustrates examples of worst and best case scenarios observed in documentation practices for healthcare delivery.
These scenarios show how the ability to copy previous entries and paste into a current entry can lead to a record where a provider may, upon signing the documentation, unwittingly attest to the accuracy and comprehensiveness of substantial amounts of duplicated or inapplicable information, as well as the incorporation of misleading or erroneous documentation.
The scenarios further illustrate that while helping to improve apparent timeliness and legibility of documentation, additional adverse effects were created by the inability to verify actual authors or to authenticate services provided at any given time.
Providers must recognize each encounter as a standalone record, and ensure the documentation within that encounter reflects the level of service actually provided and meets payer requirements for appropriate reimbursement.
The integrity of this information is vital. Poor data quality will be amplified with HIE if erroneous, incomplete, redundant, or untrustworthy data and records are allowed to cascade across the healthcare system.
Healthcare organizations must manage information as an asset and adopt proactive decision making and oversight through information asset management, information governance, and enterprise information management EIM to achieve data trustworthiness.
EIM is defined as the infrastructure and processes that ensure information is trustworthy and actionable."The Delta Model: Reinventing your Business Strategy is a work of its time. It builds on well established ideas that put the Mission Statement at the heart of strategic reasoning and expands previous thinking, for example, on the indispensable role of segmentation.
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