We’re all familiar with the concept of community or “herd” immunity. If a high proportion of a population are immunized against a communicable disease, the opportunity for the disease to spread is severely limited. The community is effectively immune, even if not every individual is.
If only it worked that way with electronic medical record (EMR) systems. Even if a high proportion of clinicians adopt an EMR, the technology can fail to deliver its objectives if a small but significant number of clinicians sometimes opt out. Any missing patient information or workflows that cannot be completed electronically hamper all EMR users. If the immunization analogy holds true, then non-adoption of the EMR can inoculate the system against success.
Not surprisingly, many of our EMR customers around the world go to great lengths to achieve universal clinical adoption. The Indonesian private hospital group Rumah Sakit Pondok Indah, for example, made sure all clinical staff used their new EMR system from day one, engaging them closely right from the system selection process. Five doctors were also employed as EMR personal trainers during system implementation. They worked alongside clinicians until everyone was comfortable using the new system.
Of course, it’s not a one-size-fits-all exercise. There are a number of clinical adoption strategies you can employ. That’s one of the reasons we recently launched an EMR adoption program, called Advance, which is based on a cycle of continuous quality improvement activities designed to help organizations achieve higher adoption levels. The program provides multiple tools and processes to identify gaps between current and desired adoption levels, and makes it easier and more rewarding for clinicians to use their EMR.
One thing all organizations can benefit from is a data-driven approach. As part of the Advance program we built clinical adoption dashboards to help customers understand their EMR usage. With any new EMR implementation, it’s common to see a decline in EMR usage within a few months after the go-live. That’s why it is important to use the dashboards to keep an eye on usage levels and intervene early. Armed with data about where the system is used and where it is not, and the percentage levels in between, organizations can better target their adoption efforts. The aim is to realize the full value of the EMR, both to the organization and individual clinical users.
Here are some examples of what clinical adoption dashboards provide:
- Overall adoption indicators displaying the percentage of the clinical record completed by different users and across all episodes, as well as episode and patient counts.
- Specialized transactional dashboards for each main clinical area, including diagnosis, observations, questionnaires, orders, allergies, alerts, patient history, problems, clinical notes, clinical summaries, and annotated images.
- User login dashboards showing trends over time including all the different system areas that the user has accessed.
Because the dashboards use the capabilities of an underlying health informatics platform, customers can apply filters and develop metrics to help them learn much more about their EMR system’s usage. They can also drill down into the dashboard graphs to see the actual usage data behind them. The data – with patient and financial identifiers removed for security and privacy – is extracted to an offline server, so the performance of the EMR is not affected.
The dashboards provide a quantitative analysis of EMR usage, which can be complemented by an online survey in which end users provide the qualitative information. Combining the information coming from the dashboards along with the information from the surveys, we are able to get a very good understanding of the issues that may be hindering clinical adoption.
Because the adoption dashboards and online surveys are free of charge, there’s no barrier to using them. There may be some differences between achieving high EMR adoption and community immunity against infectious diseases. But one thing they definitely have in common is the need to take a data-driven approach.
