The Importance of Accurate ECG Data Management in Clinical Decision-Making

The correct ECG analysis is crucial for the diagnosis and treatment choice. This statement is obvious since ECG for decades served as a reliable source of cardiac health inquiry. Yet, the process of ongoing monitoring of patients leads to another challenge of data management and adequate use in decision-making. The recent advancements in ECG technology resulted in a growing number of information that must be handled and processed effectively. To prevent possible errors and inconsistencies in the treatment of the general scope of collected data, new management practices were introduced. These practices constitute the main focus of this short review.

 

Integrity of the ECG Data and Streamlining Workflow

Effective cardiac care requires the integrity of ECG data. Among the main aspects of ECG data integrity, we can name signal quality, complete lead sets, temporal accuracy, and correct patient identification. These pillars of precise data collection and processing are obligatory to prevent managerial failure. Moreover, by prioritizing these categories healthcare providers lay a foundation for flawless clinical interpretation of the patient’s cardiac state and treatment choice. This point seems to be even more important when we refer to the transition from paper-based ECG records to digital systems. This in turn provided obvious advantages, such as instant data access, improved viewing and comparison capabilities, and integration into Electronic Health Records (EHR) and Automated Analysis solutions.

 

Advanced ECG Analysis Techniques

The modern ECG analysis techniques comprise serial ECG analysis, vectorcardiography, heart rate variability and much more. All of these techniques rely upon integration of sophisticated methods to minimize the possibility of human-made errors and ensure accurate and fast data treatment. Moreover, digitalization of the ECG data management led during the last decade to the development of the holistic approach combining machine learning with the large-scale data analysis paradigm. As a result, today we have in our hands an advanced tool for automatic metrics tracking, interpreting, and governing. This step improves patient safety significantly, while healthcare institutions get a basis for the provision of cardiac care at the greatest level of precision ever.

 

Trends and Aspirations

The aforesaid changes pave the way for gradual changes in cardiac healthcare. Accurate automated  ECG data management influences every aspect of clinical decision-making. In this regard, it is extremely important not to neglect the bridges between subjective patient experience and the digital tools responsible for this experience-making. In Norav Medical we believe that only through the correct understanding of the patients` needs and aspirations a functional link between them and the contemporary digital surroundings may be built. Making patients a central trending criterion, we aim to improve patient outcomes. Want to know more? Stay with us, or contact our Call Center, and our professional consultants will answer all your questions.

 

References:

• Kligfield, P., Gettes, L. S., Bailey, J. J., Childers, R., Deal, B. J., Hancock, E. W., … & Wagner, G. S. (2007). Recommendations for the standardization and interpretation of the electrocardiogram: part I: the electrocardiogram and its technology: a scientific statement from the American Heart Association Electrocardiography and Arrhythmias Committee, Council on Clinical Cardiology; the American College of Cardiology Foundation; and the Heart Rhythm Society endorsed by the International Society for Computerized Electrocardiology. Circulation, 115(10), 1306-1324.
  
  URL: https://www.ahajournals.org/doi/10.1161/CIRCULATIONAHA.106.180200

• Lyon, A., Mincholé, A., Martínez, J. P., Laguna, P., & Rodriguez, B. (2018). Computational techniques for ECG analysis and interpretation in light of their contribution to medical advances. Journal of The Royal Society Interface, 15(138), 20170821.
  
  URL: https://royalsocietypublishing.org/doi/10.1098/rsif.2017.0821

• Xia, T., Shu, M., Fan, H., Ma, L., & Sun, Y. (2019, November). The development and trend of ECG diagnosis assisted by artificial intelligence. In Proceedings of the 2019 2nd International Conference on Signal Processing and Machine Learning (pp. 103-107).

  URL: https://dl.acm.org/doi/10.1145/3372628.3372637