4 Health Lessons AI Can Learn from Electronic Health Records

Great joy. Great estimates. Great expectations. While these words describe today’s AI zeitgeist in healthcare, they also describe how many felt about electronic health records in the 2000s and 2010s.

Since then, nearly all US health systems and medical practices have used EHRs, improving health care in some ways and making it worse in others. The results are mixed; organizations that have invested in their employees and systems have generally done well.

AI is the next step in the decades-long digital transformation of health. Although the rollout will vary, organizations approaching AI can be better served by applying the lessons learned from implementing and using EHRs.

Lesson 1: Set Realistic Expectations

After decades of hope and hype about digital health, many expected EHRs to make health care safer, cheaper and more efficient. Things have changed in a different way.

EHRs have been a mixed bag. One, they empower nurses with light but overwhelm them with useless and useless things. For example, medical information is readable and easy to find, but often bloated with unnecessary, duplicated, and sometimes incomprehensible information.

Similarly, EHRs bring doctors and patients closer while pushing them further apart. While portals make it easy for them to communicate between visits, obtrusive screens and buttons in the exam room hinder human connection.

EHRs make physicians efficient in some ways but ineffective in others. For example, while it’s easy to prescribe medications and provide test results electronically, doctors have to deal with a lot of warnings and notifications.

Lesson 2: Put People First

Many have criticized EHRs for serving payment needs more than improving medical care. Therefore, nurses and nurses often find it difficult to use EHRs, which contributes to burnout. However organizations that put their employees first – for example by communicating clearly, investing in personal development and training – did well.

With AI, organizations must first win back the hearts and minds of patients and healthcare workers who no longer believe in the promise that more technology will improve healthcare. This will require using AI to improve outcomes and experiences (not just affordability and efficiency), making AI tools easier to use, and supporting change.

Lesson 3: Improve Monitoring Systems

Health IT does not work in isolation. It becomes part of a social technical system that includes different groups and workflows.

When implementing EHRs, many organizations implemented existing paperwork and kept their systems unchanged rather than updating them for the digital world. This resulted in inefficiencies and job losses, often forcing health workers to find solutions and perform tasks that others had previously done for them. However, organizations that have improved operational processes and teams that have re-engineered for the digital world have fared better.

Organizations must avoid making similar mistakes with AI. Bill Gates explained, “The first law of any technology used in business is that automation applied to an efficient process will increase efficiency. The second is that automation applied to an inactive process will increase efficiency.” work well.”

So, instead of rushing to automate broken systems or using AI as an aid to poorly designed technology, organizations must first improve their EHRs, streamlining work and eliminating redundant tasks. destructive. Initiatives such as GROSS can help.

Lesson 4: Continue to Invest in Change

Many organizations treated EHR implementation as a one-time event, failing to realize that it was impossible to fully anticipate what a “live” EHR would look like beforehand. immediately introduce and train their employees.

As a result, many EHR functions are cumbersome (for example, physicians in one health system must click 61 times to place a Tylenol order), and many physicians do not use the powerful features of EHR (e.g., Epic reports that only one in three physicians use its chart search. ). On the other hand, organizations that have spearheaded continuous training and EHR development have done very well.

The point is that with AI, the process should never end. Organizations should constantly monitor AI, evaluate its effects, support front-line employees, and ensure that AI-based tasks are always aligned with the work that needs to be done.

The Numbers Are Too High To Fail

Healthcare organizations can use AI to make care more accessible, efficient and effective. However success is not guaranteed. Those who apply the lessons they’ve learned to implementing EHRs — setting realistic expectations, putting people first, improving care processes and continuing to invest in change — are the ones most likely to succeed.