EMRs powered by Artificial intelligence

Advancements in medical maging and the proliferation of clinical diagnostics and screenings generate large volumes of data on patient health.

• The main challenge with EHRs for large, integrated healthcare delivery systems is that they are often considered as inflexible, difficult to use and expensive to configure.

• Data regarding care procedures, patient, administrative process, etc also cannot be captured efficiently by EHRs.

Why AI in EHR?

• AI-powered EHR systems seamlessly integrate and offer solutions with a variety of functionalities.

• Machine learning and Natural Language Processing (NLP) can help in recording the medical experiences of the patients, organizing the large EHR data banks for finding important documents, gauging patient satisfaction, etc.

• The machine learning models merged with NLP can help healthcare providers in transcribing the speech from the voice recognition system into text

• The algorithms can be trained well on large volumes of patient data on patient’s treatment, equipment used for treatment, respective doctor, etc and carefully segmented based upon the individual patient, illness, treatment for illness, etc.

• This will enhance the document and information search from the large databases.

• These applications of AI in EHR systems are broadly classified below: -

1.Extraction of data:-

• Healthcare providers can extract patient data from various sources like fax, clinical data, provider notes, etc by leveraging AI and recognize key terms that reveal actionable insights.

2.Predictive Analytics:-

• Predictive models from the big data will help to alert the physicians on potentially lethal diseases.

• AI can also power up medical image interpretation algorithms that could be integrated into the EHRs and provide decision support and treatment.

3.Clinical Documentation:-

• Healthcare companies leverage AI to develop NLP-powered tools that can integrate with the EHRs in capturing data from the clinical notes, thereby, allowing physicians to focus more on their patients and the treatments.

4.Decision Support:-

• Decisions on treatment procedures and strategies are usually generic. But with AI imbibed into the systems, more machine learning solutions that enable personalized care and learn on new and real-time data are emerging.