Technology is essential to the healthcare industry, but even though advancements have expanded diagnostic and therapeutic modalities, there remains substantial room for improvement in data management.
Background
EMRs, or electronic medical records systems, were introduced in 1972. As technology improved, many medical practices and hospitals benefited from these paperless systems, saving time and trees. However, the vast amount of digital data generated in the healthcare sector presents unique challenges that EMRs alone cannot solve.
Globally, there are 64 zettabytes of digital data, which is anticipated to double in the next two years (1). 30% of this data is related to patient medical records (2). Shortcomings in current EMR systems are hurting the quality of care, medical research opportunities, and patients’ ability to access their own health records.
Access to such a wealth of information should be an advantage, but the sheer volume of available content often proves too time-consuming for providers to sift through.
Approximately 97% go unreviewed and unutilized, preventing researchers from analyzing valuable clinical findings and physicians from providing high-quality care (3). As data continues pouring into EMR systems, the problem grows.
One proposed solution comes from an unlikely source: Artificial Intelligence.
AI systems rely on deep learning techniques that allow enormous quantities of information to be processed quickly. This increased capacity for data processing has remarkable ramifications for the medical field, especially regarding data management.
Running Out of Patience
The user interfaces of many EMR systems are complicated…so complicated that they can hinder doctors’ abilities to perform basic tasks, such as prescribing the correct dosage of medication (4). Nurses, doctors, physician assistants, and other medical professionals are well-educated, but their curricula focus on body systems and disease, not navigating software programs.
Despite many improvements to EMR systems over the decades, practitioners still click through seemingly endless data collection screens and search for elusive yet critical data. When this happens during a patient encounter, both parties can feel frustrated, leading to decreased patient satisfaction and, possibly, poor treatment compliance.
Frustration can also lead to errors like a dosage miscalculation, incorrect follow-up instructions, or worse. For instance, a Vermont attorney died from an aneurysm in 2012–just two months after her doctor recommended a (potentially life-saving) brain scan. The woman never had the scan because the physician’s order was “lost in the system” (5).
Another problem relevant to managing healthcare data is that the various EMR systems on the market don’t communicate well with one another. Whether a hospitalized patient is being transferred for specialized care or a family is moving to a new state, their medical history should be readily available to their healthcare team.
Patient records contain vital information such as detailed demographics, diagnoses, treatment plans, and prescription histories, so data isolation poses a serious problem (6).
AI Applications in Healthcare Data Management
AI’s potential to transform healthcare data management cannot be overstated. It can redefine the digital landscape, providing invaluable support to healthcare professionals and streamlining processes for improved patient outcomes.
Time & Clarity
Implementing strategic AI functions within existing EMRs can give time back to doctors and enhance clarity in their decision-making processes.
How?
Imagine an AI system trained to
- meticulously review providers’ notes,
- extract crucial context and phrases, and
- develop a summative note.
This innovation aims to reduce the time required to scan and decipher dense pieces of text, allowing physicians to find and focus on critical details they might otherwise overlook (7).
The impact of this streamlined approach goes beyond time efficiency. It allows physicians to engage more deeply with patient cases, ensuring comprehensive care and minimizing the risk of oversight. The symbiotic relationship between human expertise and AI assistance creates a synergy that elevates the quality of healthcare services.
Reducing Manual Labor
Healthcare workers often struggle to balance hands-on patient care with administrative tasks like searching extensive records and inputting patient data into EMR systems. AI, via machine learning, can alleviate this strain by recognizing user preferences over time.
This means existing EMR systems can evolve to understand and anticipate a specific user’s needs, saving precious minutes per encounter previously spent on repetitive searches and clicks (7).
Related ArticlesThe integration of AI in this regard enhances efficiency and contributes to a more user-friendly experience for healthcare professionals. As mundane tasks are automated, doctors can redirect their focus toward more impactful aspects of patient care, fostering a work environment that values time and professional expertise.
Revolutionizing Treatment Plans
AI’s ability to help extends to predictive analysis, paving the way for customized treatment plans tailored to individual patients. One groundbreaking application is the concept of therapeutic drug monitoring, wherein AI analyzes a person’s genome, predicting their reactions to specific medications with unprecedented accuracy (8).
This personalized approach to treatment represents a paradigm shift in healthcare.
No longer bound by generic prescriptions, doctors armed with AI insights can prescribe medications that align precisely with a patient’s genetic makeup, minimizing adverse effects and optimizing therapeutic outcomes.
The marriage of genetics and AI has the potential to revolutionize approaches to medical interventions, marking a significant leap forward in patient-centric care.
Continuous Patient Monitoring and Predictive Analysis
Beyond individual patient care, AI’s capabilities can contribute to positive changes in public health. By continuously monitoring patient data, AI can identify patterns and trends that are early indicators of disease outbreaks.
This proactive approach empowers healthcare systems to implement timely interventions, potentially averting widespread health crises. Moreover, analyzing vast datasets allows for a comprehensive understanding of global population health trends, enabling the development of targeted interventions and preventive measures.
Continued Advancements in Machine Learning
The journey of AI in healthcare data management is an evolving narrative. As we celebrate current successes, we must acknowledge the continuous advancements in machine learning.
The field is dynamic, with researchers and developers pushing boundaries to refine AI algorithms, enhance predictive capabilities, and expand the scope of applications.
The intersection of healthcare and technology is a fertile ground for innovation, and staying informed about emerging trends ensures that healthcare providers can leverage the latest tools to deliver optimal care.
References
-
Bartley, K. (2023, August 27). Big data statistics: How much data is there in the world? Rivery.Bartley, K. (2023, August 27). Big data statistics: How much data is there in the world? Rivery.
-
Hirschler, B. (2022, February 23). Healthcare’s data tsunami. Brunswick Group.Hirschler, B. (2022, February 23). Healthcare’s data tsunami. Brunswick Group.
-
Plescia, M. (2023, October 30). Health exec: 97% of healthcare data isn’t used. MedCity News.Plescia, M. (2023, October 30). Health exec: 97% of healthcare data isn’t used. MedCity News.
-
Willyard, C. (2020, February 1). Can AI fix electronic medical records? Scientific American.Willyard, C. (2020, February 1). Can AI fix electronic medical records? Scientific American.
-
Schulte, F. & Fry, E. (2019, March 18). Death by 1,000 clicks: Where electronic health records went wrong. KFF Health News.Schulte, F. & Fry, E. (2019, March 18). Death by 1,000 clicks: Where electronic health records went wrong. KFF Health News.
-
Ehrenstein, V., Kharrazi, H., Lehmann, H., & Taylor, O. (2019). Obtaining data from electronic health records [Electronic]. In E. Gliklich, B. Leavy, & A. Dreyer (Eds.), Tools and Technologies for Registry Interoperability, Registries for Evaluating Patient Outcomes: A User’s Guide (3rd ed.). Agency for Healthcare Research Quality.Ehrenstein, V., Kharrazi, H., Lehmann, H., & Taylor, O. (2019). Obtaining data from electronic health records [Electronic]. In E. Gliklich, B. Leavy, & A. Dreyer (Eds.), Tools and Technologies for Registry Interoperability, Registries for Evaluating Patient Outcomes: A User’s Guide (3rd ed.). Agency for Healthcare Research Quality.
-
Davenport, T.H. (2018, December 13). Using AI to improve electronic health records. Harvard Business Review.Davenport, T.H. (2018, December 13). Using AI to improve electronic health records. Harvard Business Review.
-
Alowais, S.A., et al. (2023). Revolutionizing healthcare: The role of artificial intelligence in clinical practice. BMC Medical Education, 23(689).Alowais, S.A., et al. (2023). Revolutionizing healthcare: The role of artificial intelligence in clinical practice. BMC Medical Education, 23(689).
-
Kreimer, S. (2017, November 14). Distracted doctoring? Don’t let computer come before patients during exam. American Association of Physician Leadership. https://www.physicianleaders.org/articles/distracted-doctoring-dont-let-computer-come-before-patient-during-exam
-
Lee, S., & Kim, H. S. (2021). Prospect of artificial intelligence based on electronic medical records. Journal of Lipids and Atherosclerosis, 10(3), 282–290. https://doi.org/10.12997/jla.2021.10.3.282
