Technology has been really helpful for many industries, but one industry that benefits a lot from it is healthcare. One thing that technology has made really useful in healthcare is the use of computers to keep medical records. Electronic medical records have made things easier for doctors and patients.
Healthcare organizations have important challenges to tackle, such as getting patients more involved, focusing on preventive care, coordinating different types of care, and improving diagnoses and patient results.
These are not easy tasks. Dealing with paper-based health records and files can be complicated and difficult. Many healthcare organizations struggle with these challenges because they still rely on old systems that have been in place for a long time.
EMR (Electronic medical records)
Electronic medical records (EMR) are considered a helpful way to make healthcare data flow more smoothly while allowing doctors to concentrate on enhancing patient care. While the main goal of adopting EMR/EHR is to replace paperwork, the advantages go beyond saving money and time.
One of the biggest challenges for many healthcare organizations is reducing medical errors. In numerous organizations, doctors are still manually responsible for maintaining health records, which can be a time-consuming and demanding task.
Healthcare organizations are realizing the benefits of EMR/EHR in overcoming a major obstacle in reducing medical errors. One common issue is the uncertainty surrounding whether the right medications have been given to patients.
With EMR systems, all the information about medication administration and other patient-related details is readily accessible. Managing medication administration in EMR systems doesn’t have to be a difficult task. All the necessary information about medication administration and other patient-related details can be easily viewed in one place.
AI in Addressing Physician Burnout
EMR should serve as a catalyst for quick and confident medical decision-making. However, the reality is that physicians often spend a significant amount of time engaging in tedious data entry tasks before they can focus on clinical analysis.
This situation, also known as physician burnout, is affecting doctors, patients, and administrative staff in healthcare. Many organizations still rely on doctors manually inputting information into electronic medical records, which is a demanding task. According to a study, doctors feel that digital medical records are having a negative impact on the physician-patient relationship.
They express frustration about spending more time dealing with documentation challenges than actually observing and interacting with patients.
In order to address the challenges faced by doctors in dealing with medical records, healthcare organizations are exploring the use of AI-driven, Voice-Enabled solutions to combat physician burnout.
By implementing Artificial Intelligence Powered Virtual Assistants, the burden of clinical documentation management can be eased for physicians. AI and cloud technology provide the necessary scalability and flexibility to work with modern enterprise health systems.
This solution involves the development and enhancement of language models that are customized to understand and respond to the specific commands of doctors. The solution leverages appropriate technologies, such as advanced AI and machine learning, to enhance information retrieval from electronic health records.
Achieving Interoperability Through AI and ML
Healthcare organizations are facing a growing challenge when it comes to manual data entry into EMR systems, and they are finding it increasingly difficult to handle health data interoperability.
These organizations lack confidence in meeting the requirements for health data interoperability and struggle to ensure regulatory compliance and data security. Even though organizations have adopted EMR/EHR technologies, only a small number have been able to enhance patient care through health data interoperability.
As healthcare providers recognize the difficulty of achieving interoperability, they are becoming more interested in exploring the potential of AI and ML (Artificial Intelligence and Machine Learning) technologies to address these challenges.
The healthcare team aims to analyze information from various EHR systems, but this task is challenging without a standardized approach to gather patient data. Syntactic and semantic interoperability are crucial for ensuring that information is not lost when shared among healthcare providers.
Organizations are recognizing that AI and ML technologies, with their processing speed and disruptive capabilities, can help overcome the major obstacles in addressing interoperability issues in clinical documentation. However, healthcare organizations still face ongoing challenges in improving healthcare data interoperability.
AI platforms provide a way to connect cloud-based EHR/EMR systems with external data sources, enabling interoperability. One specific challenge for the healthcare team is ensuring that all necessary forms are completed before surgeries.
This task can be difficult, but the use of AI and ML is helping organizations address the immediate priorities and needs of surgical teams. Beth Israel Deaconess Medical Center (BIDMC) is an example of an organization leveraging AI and ML to address the immediate priorities and needs of Surgery Center Physicians’ teams.
These surgical care teams need to quickly extract insights from patient data to create effective treatment recommendations. By integrating machine learning models and AI, physicians can perform calculations and receive recommendations based on surgical procedures, doctor schedules, and expected patient length of stay.
Extracting Patient Data from Unstructured Sources
The advancements in medical imaging technologies and the growing number of clinical diagnostics and screenings have resulted in a significant influx of healthcare data.
This large volume of patient data is no longer just a by-product of patient interactions; it has become a valuable asset that allows for timely processing and informed clinical decision-making. However, organizations that rely on EHR/EMR systems as the core of their integrated healthcare delivery systems are facing challenges.
These systems are often inflexible and not user-friendly, making it difficult for healthcare providers to navigate and utilize the data effectively.
The integration of AI and ML in EMR/EHR systems allows healthcare organizations to break down data silos and uncover valuable clinical insights from both structured and unstructured data.
AI utilizes this combined data to streamline processes, generate meaningful insights, and provide a comprehensive view of a patient’s health. This is crucial because physicians can engage more effectively with patients when they have access to relevant and actionable patient data.
By leveraging AI and ML, EMR/EHR systems offer the necessary flexibility and user-friendliness to adapt to the ever-increasing complexity of clinical care in today’s healthcare landscape.
A significant portion of health data, such as health records, trail reports, and physician’s notes, exists in formats that cannot be easily processed using traditional methods.
Manually extracting patient data from these unstructured sources requires a lot of resources and time. Rule-based approaches to data extraction may not be effective if they don’t consider the context.
However, cloud-based AI solutions provide healthcare organizations with the ability to extract valuable clinical insights from unstructured medical text and extract relevant medical information. Powered by advanced machine learning models, this cloud-based solution simplifies the process of leveraging patient data in healthcare effectively.
Effective Healthcare Revenue Cycle Management with EMR
EMR brings numerous advantages to healthcare, but one significant advantage, and perhaps the primary reason for its implementation, is the streamlining of the healthcare revenue cycle and the improvement of overall healthcare quality.
Effective management of the healthcare revenue cycle is crucial for the success of healthcare organizations. Inaccurate coding or errors in health records are major contributors to insurance providers rejecting a significant portion of medical claims. EMR/EHR systems address these areas comprehensively with a systematic approach, reducing the risk of medical errors and improving the accuracy of coding and health records, leading to a more efficient revenue cycle management process.
Conclusion
The process of modernizing medical records in healthcare was already underway even before the pandemic. However, healthcare organizations and physicians were initially slow in adopting advanced technologies to revolutionize patient care.
Nevertheless, the COVID-19 pandemic has acted as a catalyst, accelerating the adoption of emerging technologies such as AI, ML, and Blockchain in the realm of patient care. These technologies have now become central to healthcare, playing a crucial role in transforming and improving the delivery of care to patients.
