2. Challenges of electronic health record systems – professional and data quality viewpoints

The intent behind the design of electronic health record (EHR) systems was to facilitate patient care and management but as time progressed, the EHR systems evolved to be used for many additional purposes. Among other things, EHR systems are used to enforce compliance with organizational directives or regulation, collect data for public health purposes, for research and as a source of billing documentation. All these other purposes and requirements on EHR systems have increased the clinicians’ burden. Furthermore, there are partially competing needs from different services and stakeholders, sometimes concerning even the same data. All groups must be involved to ensure that these different needs are addressed in a balanced way.

The burden for healthcare professionals includes numerous challenges and stresses that they face in their daily work. The challenges are primarily cognitive in nature and involve cognitive processes, working and long-term memory, comprehension, problem-solving and decision-making, which are all essential and integral aspect of clinical practice. The challenges and stresses can be caused by different situations and include administrative tasks, electronic health records management, increased workload, complex medical cases, emotional toll, and lack of adequate support systems. These can further lead to burnout, reduced job satisfaction, and decreased quality of care for patients. Addressing these problems is important for promoting the well-being of healthcare professionals and ensuring the delivery of high-quality care to patients.

The many different purposes and requirements faced by EHR systems have increased the healthcare professionals’ burden by several mechanisms:

Some challenges which create cognitive barrier to accessing and using patient information effectively can be summarised by:

(Patel V.L., Cognitive Challenges in the Use of EHRs. HL7 Working Group on Clinical Burden, Virtual Presentation April 24, 2023).

By focusing on a socio-technical approach on design and implementation of EHRs as to ensure that the user interface is intuitive and easy to navigate will reduce the stress felt by healthcare professionals. Strategies such as simplifying tasks and instructions, providing adequate training and feedback, and minimizing distractions will reduce the extraneous cognitive load. E-Health standards must be able to help with interoperability issues, filtering and showing only relevant information to the clinician and reduce the need for double documentation and data entry errors. (Patel V.L., Cognitive Challenges in the Use of EHRs. HL7 Working Group on Clinical Burden, Virtual Presentation April 24, 2023)

Data quality is increasingly considered as a critical success factor for primary and secondary use of health information

There are different definitions for primary and secondary use of health data. In EHR context, primary use has been defined mainly in relation to services and personnel involved in providing health care and secondary use considers organizational management, health research, innovation, education policymaking, regulatory purposes or supervision. In comparison, some sources such as GDPR regulation define primary use as any purpose for which data is originally gathered, including collection of data specifically for research or supervision.

. Data quality in healthcare refers to the completeness, accuracy, timeliness, consistency, and reliability of data collected, stored, and utilized within the healthcare system. High-quality data is crucial for effective healthcare delivery, decision-making, research, and patient safety. Challenges related to data quality are frequently interwoven with healthcare professionals’ burden, and often share the same underlying issues - and therefore - often the same solution.

Completeness refers to capturing all relevant data and is essential for a comprehensive understanding of patient health. However, capturing all relevant data elements can be challenging or impossible due to variations in documentation practices, missing or omitted information, or incomplete data transmission between different healthcare systems. In addition, relevance cannot always be assessed for all possible needs in mind.

Maintaining accurate data is a primary challenge. Errors can occur during data entry, coding, or transcription, leading to incorrect or incomplete information. Inaccurate data can compromise patient safety, lead to inadequate clinical decisions, and impact research or management decision making outcomes.

Consistency refers to the standardization of data across various sources and systems. In healthcare, data consistency can be affected by variations in terminology, coding systems, data formats, or data entry practices. Inconsistencies can lead to difficulties in aggregating and comparing data, hindering accurate analysis, and reporting. Inconsistency also hinders utilization and integration of added-value tools such as clinical decision support.

Timely data capture is crucial for real-time decision-making and patient care. However, delays in data entry, transmission or retrieval can impact the timeliness of information. Outdated or delayed data can result in suboptimal clinical decisions and hinder public health surveillance efforts.

Healthcare organizations often use disparate EHR systems, medical devices and databases that may not seamlessly communicate or exchange data. Lack of interoperability can hinder data sharing, integration, and continuity of care, leading to fragmented and incomplete patient records in healthcare systems.

In addition to exchange of data, semantic interoperability must be considered. Bottlenecks in semantic interoperability are often related to unclear terminology, missing definitions of central concepts or local or organization-specific variations in code systems, for example. The slow adoption of e-Health standards and specifications in (legacy) systems further hinders achieving interoperability on many levels.

Establishing effective data governance frameworks, policies and procedures is crucial for maintaining data quality. Healthcare organizations must define various aspects of standards for data, enforce data quality control measures, provide training, and promote data stewardship to ensure consistent and reliable data across the healthcare system. Addressing these challenges requires a multifaceted approach involving technological advancements, standardized data models, improved documentation practices, enhanced data governance, and collaboration among healthcare stakeholders. It is an ongoing process that requires continuous monitoring, quality assurance, and improvement efforts to ensure reliable and high-quality healthcare data.