An Electronic Health Record (EHR) system must enable efficient availability of meaningful, accurate and complete data to assist improved clinical administration through the development, implementation and optimisation of clinical pathways. Therefore data integrity is the driving force in EHR systems and is an essential aspect of service delivery at all levels. However, preserving data integrity in EHR systems has become a major problem because of its consequences in promoting high standards of patient care. In this paper, we review and address the impact of data integrity of the use of EHR system and its associated issues. We determine and analyse three phases of data integrity of an EHR system. Finally, we also present an appropriate method to preserve the integrity in EHR systems. To analyse and evaluate the data integrity, one of the major clinical systems in Australia is considered. This will demonstrate the impact on quality and safety of patient care.
Today, more than ever, organisations realise the importance of data quality [1] because of the introduction of increased reliance upon networked data. However one of the serious problems in depending on networked data is 'dirty data'. Dirty data may include incomplete, missing or inaccurate information.
The concern is particularly significant in health care, where dirty data represents the dark side of the great potential offered by the adoption of health related IT systems. First and foremost, dirty data can lead to medical errors, which can kill or cause long-term damage to the health of patients. Data should be an accurate representation of its source. It should be reliable. Data should have internal consistency. Data should adhere to rules based on the logic of the real world. The accuracy, internal quality, and reliability of data are frequently referred as data integrity [2]. This means the enforcement of data integrity ensures the quality of the data.
In EHR, data integrity entails the accuracy of the complete health record’s documentation. It encompasses information governance, patient identification and validation of authorship and record amendments. Furthermore the quality of data contained in an EHR is dependent on accurate information at the point of capturethe data source. For example, the Table1 shows the potential EHR risks and how these risks impact data integrity in healthcare.
As Table 1 exemplifies, while a primary goal of EHR implementation is the reduction of medical errors, reports of new types of errors directly related to EHR implementation that can compromise quality of care and patient safety have emerged [5].
Inaccurate health information may adversely affect the quality of an individual’s healthcare. Maintaining the integrity and completeness of A baby died from a massive drug overdose as a result of a transcription error that occurred when a handwritten order was entered into the computer system. However this medical error could have been prevented if automated alerts had been activated [4]
A structured data field may indicate that one pill should be taken twice a day, while the free-text filed says to take two pills in the morning and one pill in the evening
Copying and pasting the same note accidentally for several rows may result in the same medication or condition repeated unnecessarily.
Templates automatically fill in data elements based on other data entries before clinicians complete the actual data. Clinical environment may contribute to the occurrence of clinical decision support system error.
User distraction might cause data entry errors or inattentiveness to the information being presented by the decision support system.
health data is paramount because the computerisation of health information grows and the scope of organisational exchange of health information widens into Health Information Exchanges (HIEs). Patient identity integrity is the accuracy, quality, and completeness of demographic data attached to or associated with an individual patient. This includes not only the accuracy and quality of the data as it relates to the individual, as well as the correctness of the linking or matching of all existing records for that individual within and across information systems. The quality of healthcare across the continuum depends on the integrity, reliability, and accuracy of health information [6,27,36,37,38,39].
With the continued advancement of electronic health records (EHRs), there 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.
Poor system usability including inappropriate EHR design gets in the way of face-to-face interaction with patients and health care providers are forced to spend more time documenting required health information for the EHR. Features such as pop-up reminders, cumbersome menus and poor user interfaces can make EHRs far more time consuming than paper charts [7]. Poor system interface problems also can lead to poor decisions. For example, a laboratory value may come back with an extra character inadvertently inserted [8].
Inappropriate use of EHR can also result in potential data integrity issues. For example copy and paste or cloning can lead to redundant and inaccurate information in EHRs. Using this feature can cause authorship integrity issues since documentation cannot be tracked to the original source [9,[33][34][35].
The software system vendors often add functionalities to assist with documentation capture such as templates, use of standard phrases and paragraphs and automatic object insertion to improve efficiency of data capture, timelines, legibility, consistency and completeness of documentation.
However, when used inappropriately, without proper education and controls, these features can lead to inaccurate documentation and potentially result in medical errors or allegatio
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