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The Copy & Paste Problem in EHR Patient Documentation

Skyrocketing rates of physician burnout and provider frustration with the time required to enter records into the EHR have driven the widespread use of copy and paste when entering patient information into electronic health records.

Back in 2017, a study published by the Journal of the American Medical Association reviewed thousands of EHR records and found that only a small minority of them were manually entered – but more than 80 percent of the notes were imported or copied from elsewhere.

The team analyzed 23,630 inpatient progress notes written by 460 caregivers who were direct care hospitalists, residents and medical students.

Researchers found that 46 percent of notes were copied, 36 percent were imported, and just 18 percent of the text was entered manually.

Accuracy, security and patient safety are all put in peril when copying and pasting from one patient note into another. Additionally, inaccurate and incomplete records can result in delayed or denied reimbursements for the practice.

The ECRI Institute published its “Health IT Safe Practices: Toolkit for the Safe Use of Copy and Paste” guidelines in 2016 in an effort to educate the medical community about best practices for utilizing copy and paste in EHR documentation.

According to the 58-pages of the ECRI Institute’s guidelines, adopting safer copy-and-paste practices would require each practice to implement a series of cumbersome steps, including establishing new levels of staff training and oversight, flagging pasted material for easy identification, distinguishing between appropriate and inappropriate times to copy/paste, and more.

It’s clear that putting the new processes into action could undermine any speed improvements derived from copying and pasting.

Fortunately, there is a better way to enter patient notes.

NoteSwift (founded by a practicing physician) has developed an A.I.-driven EHR Transcriptionist – Samantha – that simplifies patient note entry and eliminates the need to copy data.

Samantha takes the physician’s narrative input (either typed manually, or from any medical speech recognition tool) and intelligently parses the information to identify structured data elements, assign required codes, and present the complete patient note to the user for verification. Samantha then enters the entire patient note into the correct menus, fields, and check-boxes of the EHR automatically, saving time, virtually eliminating clicks, and ultimately helping to reduce physician burnout.

With Samantha, the increased speed encompasses all aspects of the patient note, including entering narrative text and structured data, navigating from screen to screen, looking up complaints, and completing and sending prescriptions and lab orders.

Want to see Samantha in action? Contact us to schedule a live demonstration for your practice.

 

Three Ways A.I. is Improving EHR Performance

NoteSwift CEO Wayne Crandall discusses “Three Ways A.I. is Improving EHR Performance” on Healthcare IT Today this week.

From the article:

“Let’s be honest — we can all see the immense promise of a healthcare world fully connected through EHR systems, but first (and even second) generations of EHR software have not yet achieved that promise. Many practices have had the opposite experience; instead of benefits and promises, their EHR systems have been frustrating, cumbersome, and difficult to manage.

But don’t give up! Advancements in artificial intelligence are rapidly improving the ability of EHRs to become more accurate, more user-friendly, and more connected.

Here are three major ways artificial intelligence is improving the world of EHR:

  1. Advances in Voice Dictation for Note Entry

Voice dictation is already a valuable tool for many doctors to make EHR entry take less time — in fact, 62% of doctors already use voice dictation software to assist with their EHR entry, and nearly another 20% have plans to add voice dictation to their workflow in the coming year. Natural Language Processing (NLP) continues to make voice dictation more accurate and more useful for physicians and staff.

  1. Structured Data Elements

Long the Achilles heel for EHR entry, structured data elements are the vital data formatting structure that allows practices to use their EHRs to achieve Meaningful Use and participate in HIE requirements. According to many researchers, unstructured data is the cause of much of the adoption and transition pain practices feel around EHR use. Many practices simply dump old data into their new EHRs, which both fails to meet MU requirements and reduces the physician’s ability to actually use the data to improve patient care. A.I.-powered solutions such as Samantha, the real-time EHR transcriptionist from NoteSwift, are using artificial intelligence to turn dictated patient narratives into structured data at the time of entry.

  1. Heuristic Learning and Clinician Review

A recent study noted that there is currently a 7.4% error rate in voice dictation data entry when not supported by software optimization and clinical review tools. Thankfully, artificial intelligence solutions are rapidly improving the accuracy of dictation while also offering clinicians more robust tools for quickly and effectively reviewing and approving EHR entries. Samantha from NoteSwift features A.I.-powered heuristic learning technology that improves clarity and accuracy of the EHR entry every time a physician uses the solution. So, the more you use it, the better it performs.”

Read the full post on Healthcare IT Today.

Ready to see how Samantha’s A.I.-powered technology saves physicians 6-8 hours a week over traditional documentation methods? Contact us to schedule a live demonstration of Samantha, the real-time EHR transcriptionist.

 

NoteSwift Nominated for Speech Technology People’s Choice Awards 2019

NoteSwift is proud to be nominated by Speech Technology Magazine for their 2019 People’s Choice Awards.

NoteSwift is nominated in two categories for our revolutionary, real-time EHR Transcriptionist, SamanthaTM:

  • “Artificial Intelligence / Machine Learning / Natural Language,” and
  • Virtual Assistants”

Read more