Being a medical transcription company with relevant experience in the industry, we discussed in our previous blog about a study proving that stress from electronic health records (EHR) is leading to physician burnouts. Switching to electronic records has brought myriad problems associated with cognitive overload, endless documentation, and user burnout. To meet such issues, EHR vendors are now using AI to create more intuitive interfaces and automate some of the routine processes that consume so much of a user’s time. Most common causes of EHR-related stress reported in the study include too little time for documentation, time spent at home managing records and EHR user interfaces that are not intuitive to the physicians who use them.
An ideal and promising option is to use Artificial intelligence (AI) to make existing EHR systems more flexible and intelligent. In collaboration with their EHR platform vendor, hospitals are also making use of AI tools. For instance, Health Data Management discusses the case of Halifax Health in Daytona Beach that is using artificial intelligence applications to augment its electronic health record system, which are improving workflows and the quality of documentation.
A viewpoint article published earlier in JMIR Medical Informatics has found that physicians can find some relief from burnout as artificial intelligence moves closer to reality. Becker’s Hospital Review has recently reported about Amazon’s new EHR-mining tool- Comprehend Medical that uses natural language processing and machine learning – two types of artificial intelligence – to review EHRs and unstructured clinical notes to highlight data that may help physicians improve patient care.It allows developers to process unstructured medical text and identify information such as patient diagnosis, treatments, dosages, symptoms and signs, and more. The software may also free employees of clerical work, such as manually rifling through notes.
AI capabilities for EHRs include
Extracting data from text
AI will help extract value from EHRs, taking patient care to the next level and ultimately boosting the organization’s bottom line. Amazon’s new Comprehend Medical AWS cloud service is one such natural-language processing tool that can read physician notes, patient prescriptions, audio interview transcripts, and pathology and radiology reports – and it uses machine learning algorithms to spit out relevant medical information to healthcare providers.This EHR-mining software extracts and index data and relevant information from clinical notes as well as patient records.
Alerts and predictions
Often EHRs are used to alert clinicians when entered information meets certain conditions, leading to further examinations and treatments. As this is time consuming, EHR vendors are now introducing prediction models from big data that can warn clinicians of high risk conditions such as sepsis and heart failure. AI enables healthcare teams to develop protocols within just 10 days, providing operational capabilities and delivering quality care. AI tools also help to process routine requests, medication refills and result notifications. Users can also prioritize their tasks, making it easier to work through their to-do lists.
Clinical documentation and data entry
Artificial intelligence (AI) could help with capturing clinical notes, automating documentation and data entry. Its speech recognition and natural language processing technology can support the creation of notes in real time by listening in on patient-physician conversations. Such tools can also collect, sort, and assemble clinical information from multiple sources. Clinicians can focus on their patients rather than keyboards and screens. AI-enhanced software can also analyze a note’s content and provide real-time evidence-based recommendations to physicians using dynamic clinical data mining.
Clinical decision support
Clinical decision support (CDS) assists care providers with knowledge that can enhance the health of their patients. Clinical decision support systems that do not use AI have struggled to improve major patient outcomes. Machine learning can enable clinical decision support in EHRs to understand which patients are at highest risk of a negative outcome, or to optimize treatment in real-time, to remind clinicians to record height and weight to calculate BMI, or to perform medication reconciliation at each office visit. Vendors such as IBM Watson, Change Healthcare, AllScripts are considering AI for clinical decision support that enables more personalized care. Amazon’s AI tool’s insights can help healthcare organizations with clinical decision support, revenue cycle management and population health.
AI can also help physicians with point-of-care learning as well as quality-measurement reporting. While considering outsourcing options for EHR integrated medical transcription services, make sure that the company you partner with is aware of the advanced AI technologies to help manage your EHR.