Using AI and ML in EMR/EHR to improve care interventions and patient outcomes
The advancements in technology have come as an advantage to all industries. However, one industry that stands to gain from the technology is healthcare. If there is one thing that technology made impressively useful in healthcare, then it has to be the computerization of medical records. Electronic medical records have made life easier for physicians and patients.
Organizations are looking to meet challenges that include better patient engagement, preventive care, and consolidated care, and improved diagnostics, and patient outcomes – no small task. Managing paper-based health records and files can be complex and challenging. Many healthcare organizations face these challenges because most have legacy systems at the center of everything they do.
EMR is seen as an effective step in streamlining the flow of healthcare data while enabling physicians to focus more on improving patient care. Although replacing paperwork may be the main motive behind embracing EMR/EHR, the benefits extend beyond just cost and time savings. For most healthcare organizations, reducing medical errors is one of the most significant challenges. In many organizations, maintaining health records is still an intensive manual task that physicians perform.
Healthcare organizations are discovering how EMR/EHR can help them overcome one of the most significant barriers to reduce medical errors. Health teams often get caught up in the guesswork of not knowing whether appropriate prescriptions have been administered. All the information pertaining to the medication administration and other patient-related information is instantly available. Administration of medication in EMR systems doesn’t have to be painful. All the information pertaining to the medication administration and other patient-related information is available at a glance.
AI in addressing physician burnout
EMR should be the fuel for rapid and confident medical decision-making. Unfortunately, physicians spend much of their time doing arduous data entry routines before getting on with the clinical analysis. Also known as physician burnout, electronic health record stress in healthcare is taking a toll on doctors, patients, and administrative staff. In many organizations, feeding electronic medical records is still an intensive manual task that doctors perform. According to a study, doctors believe that digital medical records are negatively impacting the physician-patient relationship. Doctors complain about spending more time dealing with clinical documentation challenges than observing and interacting with the patient.
As doctors in the medical industry continue to face challenges in addressing medical record challenges, healthcare organizations explore AI-driven, Voice-Enabled solutions to address physician burnout. With Artificial Intelligence Powered Virtual Assistants, clinical documentation management doesn’t have to be painful for physicians. AI and cloud technology offer the scale and agility needed to work with today’s enterprise health systems. This solution involves developing and improving language models that are then adapted to match doctor’s personalized commands. The solution leverages the right tech to improve information retrieval from electronic health records into advanced AI and machine learning technologies.
Achieving interoperability through AI and ML
Healthcare organizations seem to face an increasingly complex task of manually entering data into EMR, and they have become more complicated to cope with health data interoperability. Organizations are far from confident in successfully meeting health data interoperability needs and maintaining regulatory compliance & data safety. Despite organizations adopting EMR/EHR technologies, only a few have improved patient care with health data interoperability. As healthcare providers understand the challenge of achieving interoperability, they shift their interest towards AI and ML.
The health team’s goal is to analyze information from disparate and diverse EHR systems. However, analyzing data from multiple EHR systems can’t be successful without a common standard for gathering patient data. Organizations must have syntactic and semantic interoperability to ensure information is not lost when shared between healthcare providers. Organizations are discovering how the processing speed and disruptive power of AI and ML are overcoming one of the most significant barriers to address interoperability issues in clinical documentation. Healthcare organizations continue to face challenges in improving healthcare data interoperability.
AI platforms enable cloud-based EHR/EMR infrastructure interoperability with external data sources. The health team has to ensure that all necessary forms are completed ahead of surgery. However, it is challenging. Leveraging AI and Ml is helping organizations to address health teams’ immediate priorities and needs during surgeries. Beth Israel Deaconess Medical Center (BIDMC) leverages AI and Ml to address Surgery Center Physicians teams’ immediate priorities and needs. Surgical care teams must quickly glean insights from patient data to create treatment recommendations that work. The Physicians’ teams are integrating machine learning models and AI to perform calculations and make recommendations leveraging surgical procedures, doctor’s schedules, and patient length of stay.
Extracting patient data from unstructured sources
The rise of advancements in medical imaging technologies and the rapid increase in clinical diagnostics and screenings lead to a massive surge in healthcare data. The large volume of patient data is no longer a by-product of patient interaction; it’s a critical asset that enables timely processing and sound clinical decision-making. However, organizations that have EHR/EMRs at the center of large, integrated healthcare delivery systems are struggling because they are inflexible and not intuitive.
AI and ML in EMR/EHRÂ empowers businesses to disrupt data silos and find new clinical data insights from structured and unstructured data. AI leverages structured and unstructured data from EHR/EMR to streamline processes, provide insights, and deliver a complete view of patient health. Because only with relevant and actionable patient data can physicians most effectively engage with patients. AI and ML in EMR/EHR offer the flexibility and intuitiveness needed to work with today’s increasing complexity of clinical care.
A good amount of health data (health records, trail reports, physician’s notes) is in a format that cannot be processed by traditional means. The process of manually extracting patient data from unstructured sources is resource-intensive and time-consuming. At the same time, rule-based data extraction can’t be successful if it doesn’t take context into account. Cloud-based AI solutions enable healthcare organizations to extract valuable clinical insights from unstructured medical text extract medical information. Driven by state-of-the-art machine learning models, this cloud-based solution makes it easier to leverage patient data in healthcare successfully.
Effective healthcare revenue cycle management with EMR
There are many advantages of EMR in healthcare; however, one big advantage and probably one of the main reasons for implementing EMR is to streamline the healthcare revenue cycle and improve overall healthcare quality. Effective Healthcare revenue cycle management is the difference-maker between successful healthcare businesses and others. Inaccurate coding or mistakes in health records are the leading cause why insurance providers reject half of the medical claims. EMR/EHR covers all of these areas in depth with a systematic approach to reduce the risk of medical errors.
Conclusion
Even before the pandemic, modernizing medical records in healthcare has already existed. Healthcare organizations and physicians may have been slow to implement advanced technologies in revolutionizing patient care. However, the COVID-19 pandemic accelerated adoption and placed emerging technologies like AI, ML, and Blockchain at the heart of patient care.
As data explodes and advanced technologies come into play, healthcare organizations need to have a culture of incorporating disruptive technologies like AI and ML into EMR/EHR to revolutionize patient-centered care.
