Using AI to Improve Electronic Health Records
Electronic health record systems for large, integrated healthcare delivery networks today are often viewed as monolithic, inflexible, difficult to use and costly to configure. As delivery networks grow and deploy broad enterprise EHR platforms, the challenge of making them help rather than hinder clinicians is increasing. A promising approach is to use AI to make existing EHR systems more flexible and intelligent. Some delivery networks are making strides in this direction, using AI to assist with data extraction from free text, clinical documentation and data entry, and clinical decision support. Ultimately, AI should help doctors tailor EHRs to their specific needs and work styles making them easier to use and more valuable in the care process. That could help reduce clinician burnout and improve patient outcomes.
Electronic health record systems for large, integrated healthcare delivery networks today are often viewed as monolithic, inflexible, difficult to use and costly to configure. They are almost always obtained from commercial vendors and require considerable time, money, and consulting assistance to implement, support and optimize.
The most popular systems are often built around older underlying technologies, and it often shows in their ease of use. Many healthcare providers (including the surgeon and author Atul Gawande) find these systems complex and difficult to navigate, and it is rare that the EHR system is a good fit with their preferred care delivery processes.
As delivery networks grow and deploy broad enterprise EHR platforms, the challenge of making them help rather than hinder clinicians is increasing. Clinicians’ knowledge extends far beyond their clinical domain—care procedure knowledge, patient context knowledge, administrative process knowledge—and it’s rare that EHRs can capture all of it efficiently or make it easily available. What’s more, in the U.S., regulatory, billing and revenue cycle requirements add additional complexity to the electronic healthcare workflow and further reducethe time clinicians have to engage with patients.
The options for improving this misalignment between systems and processes are limited. One is to design EHR systems to be more integrated and streamlined from the beginning. One Medical, for example, a concierge medical practice across 40 cities in the U.S., developed its own EHR system that is closely aligned with the care and patient relationship practices it employs. Flatiron Health, a data and analytics-driven cancer care service recently acquired by Roche, bought a company with a web-based EHR and tailored it to fit its OncoCloud EHR for community-based oncology. Although these bespoke systems do seem to fit clinician workflows better, they are themselves difficult and time-consuming to develop (One Medical required ten years to build its system) and they are relatively narrow in scope. Building a system from scratch or extensively customizing a commercial one would probably not work for large delivery networks.
Using an open source EHR is a second option. However, most current ones are designed for small medical practices and aren’t easily scalable or need substantial configuration. And even though the software is free, considerable programming and IT infrastructure is required to implement it and tailor it to the individual practice. Further, open source EHRs are less carefully maintained and less frequently updated than commercial ones and so can quickly become obsolete. Finally, regulatory requirements and reimbursement rules change rapidly. Relying on either open source or internally developed systems in keeping up with those requirements creates both compliance risks and financial challenges.
A third and more promising option is to use AI to make existing EHR systems more flexible and intelligent. Some delivery networks, sometimes in collaboration with their EHR platform vendor, are making strides in this direction.
