My healthcare system is strategically transitioning to an upgraded Information management system (IS). Transition is being met with a mix of anticipation and anxiety. We humor ourselves that anything is an upgrade from our current system, but in reality, there is a palpable level of anxiety during this time of change, even with preparation and support. As a healthcare provider who prioritizes evidence-grounded practice, high quality, accurate data is vital as I navigate through a myriad of treatment options and clinical decisions made with and for my patients. Our existing system, while not perfect, is familiar, and change carries inherent uncertainties. However, as a Provider Champion involved in the preparation for this significant shift, I am optimistic that our new system will enhance our ability to deliver quality patient care by providing us with higher quality, more accurate, and real-time data.
The significance of data in any municipal function, including healthcare, is paramount. Data is comprised of raw facts without context, relevance or application. Once contextualized, data develops into knowledge and wisdom, which are critical for advancing understanding and enhancing effective problem solving. Data forms the foundation of the Data-Information-Knowledge-Wisdom (DIKW) paradigm, transforming raw facts into actionable knowledge1. The quality of the data is instrumental in this process, making it crucial to assure its integrity2.
Regrettably, data quality can be compromised in numerous ways, such as through initial input errors, storage issues, transmission problems, and output inconsistencies3. These defects can undermine decision-making processes and hinder the achievement of desired outcomes. Therefore, it is fundamental for municipal leaders to ensure the cleanliness and reliability of their data.
Electronic information systems provide repositories for memorializing attained past data, for providing access to relevant current data, and for inferring or predicting future outcomes through human or artificial intelligence. When operating systems issues arise, the issues or unexpected poor outcomes result from (a) the electronic data itself, (b) the users, or (c) both a and b. A well-designed data system can facilitate this process by reducing the risk of human errors, enhancing clinical decision-making, and providing valuable, timely, and relevant information4. Furthermore, a system that closely aligns with the user's specific activities and fosters a sense of comfort and competency is more likely to be well received5.
However, achieving optimal usage of an electronic data system requires more than just a well-designed system. Users must be receptive to change and possess a commitment to lifelong learning6. They must also be capable of effectively obtaining, processing, synthesizing, and communicating information7.
As we transition to our new IS, we must ensure that it meets the five rights of Information Systems, namely the right information, accessibility, settings/location, application, and timing8. It must also adhere to clinical guidelines, be user-friendly, and easily integrate into existing practices9.
But how do we ensure the successful adoption and integration of our new information system? It begins with stakeholder engagement and understanding their needs. We must ask for a commitment to change and clarity on what data is meaningful and how it should be managed. Adequate training is also crucial, as is the availability of trainers or superusers in real time10.
As we continue our transition to the new IS, it is my hope that we will all embrace the change, commit to learning, and utilize the system to its fullest. The successful implementation of the new IS represents an opportunity to improve patient care through the use of accurate, high-quality data. Fingers crossed!
References
Mastrian, K.G. & McGonigle, D. (2021). "Informatics for Health Professionals (2nd ed.)." Burlington, MA: Jones & Bartlett Learning.
McGonigle, D., Mastrian, K., & McGonigle, C. (2021). "Chapter 2: Introduction to Information, Information Science, and Information Systems." In K.G. Mastrian and D. McGonigle (eds.), Informatics for Health Professionals (2nd ed.) (pp 19-30). Burlington, MA: Jones and Bartlett Learning.
Ibid.
McGonigle, D., Mastrian, K., & McGonigle, C. (2021). "Chapter 2: Introduction to Information, Information Science, and Information Systems." In K.G. Mastrian and D. McGonigle (eds.), Informatics for Health Professionals (2nd ed.) (pp 19-30). Burlington, MA: Jones and Bartlett Learning.
McGonigle, D., Mastrian, K., McGonigle, C., & Kaminski, J. (2021). "Chapter 3: Computer Science and the Foundation of Knowledge Model." In K.G. Mastrian and D. McGonigle (eds.), Informatics for Health Professionals (2nd ed.). Burlington, MA: Jones and Bartlett Learning.
Mastrian, K.G. & McGonigle, D. (2021). "Chapter 1: Informatics, Disciplinary Science, and the Foundation of Knowledge." In K.G. Mastrian and D. McGonigle (eds.), Informatics for Health Professionals (2nd ed.) (pp 5-18). Burlington, MA: Jones and Bartlett Learning.
Ibid.
McGonigle, D., Mastrian, K., & McGonigle, C. (2021). "Chapter 2: Introduction to Information, Information Science, and Information Systems." In K.G. Mastrian and D. McGonigle (eds.), Informatics for Health Professionals (2nd ed.) (pp 19-30). Burlington, MA: Jones and Bartlett Learning.
Moghadam, S.T., Sadoughi, F., Velayati, F., Ehsanzadeh, S.J., Poursharif, S. (2021). "The effects of clinical decision support system for prescribing medication on patient outcomes and physician practice performance: a systematic review and meta-analysis." BMC Medical Informatics and Decision Making, 21 (98), n.p.
Ibid.