- Long before meaningful use made it financially attractive to implement electronic health records, EHR advocates were making the argument that improved information technology was critical for healthcare data integrity and all of the clinical analytics, population health management, and personalized care activities they hoped would naturally follow.
EHRs would become the foundation of an interoperable data exchange ecosystem that could cut patient safety risks, predict hospital admissions and other serious events, and ensure that complete and up-to-date healthcare data was available when and where it was needed.
Any healthcare provider – and any patient, for that matter – knows that the industry has not yet managed to achieve this vision despite the near ubiquitous presence of EHRs in hospitals and physician offices.
While there are still dozens of reasons why EHRs have not lived up to their revolutionary promise, a widespread lack of healthcare data integrity remains one of the primary roadblocks.
Poor EHR optimization and haphazard implementation and enforcement of data governance principles make the old mantra “garbage in, garbage out” painfully applicable to healthcare. For years, lackluster EHR data integrity stemming from unwieldy interfaces, cumbersome workflow processes, and questionable workarounds has ranked among the industry’s top IT hazards, producing downstream effects on patient matching, clinical decision-making, data analytics, provider productivity, and patient safety.
How can healthcare providers stop making EHRs the enemy? By following these four steps for optimizing their technology systems and healthcare data integrity guidelines to produce clean, comprehensive, meaningful data.
Assess your technology tools…and your technology users
Most EHRs profess to be perfect right out of the box, but few healthcare organizations truly benefit from one-size-fits all technology solutions. Whether you’re starting with a blank slate that doesn’t quite meet your needs or have made so many tweaks that the EHR interface is cluttered and clogged with years of changes, having a clear idea of what your EHR can do – and what you need it to do – is a vital first step.
Be sure to ask all EHR stakeholders, including physicians, nurses, and administrative staff, what they want to be able to accomplish with the EHR. Some users may prefer free-text input while others like check-boxes and drop-downs. Some can’t stand pop-up alerts while others find they make it easier to remember things. While no EHR will ever satisfy everyone, gathering opinions can help to establish a direction for an optimization program.
However, even the most intuitive and seamless EHR experience can be rendered inefficient by a user who doesn’t understand how the process works or circumvents it in an effort to save time. Be sure to identify any short-cuts that your end users are taking. Establish a non-judgmental, non-punitive environment to tease out those workarounds that may go against current organizational policy. You need to make sure that you have an accurate picture of what is really going on during data entry, even if clinical staff might be reluctant to share.
Establish robust data governance policies
Once you have a better idea of what your organization needs, you can begin to stamp out unhelpful practices with the establishment of strong and meaningful healthcare data governance policies. Up to forty percent of organizations have no formal data governance strategy, HIMSS found in 2014, and only two-thirds of providers that do have a plan also have a multi-stakeholder committee to approve changes and make suggestions.
“How organizations make decisions around enhancements to EHRs, including implementation can dramatically impact their ability to meet regulatory measures and create workflow efficiencies,” said HIMSS Analytics Research Director Brendan FitzGerald.
For better health information management and data integrity, organizations may wish to consider enlisting the help of a health information management professional well-versed in industry standards for data optimization that work towards preparing an organization’s data to engage in clinical analytics, population health management, and personalized, patient-centered care.
Important policies to consider include rules about copying and pasting or “cloning” EHR information, the use of shorthand or abbreviations that may be confusing or carry multiple meanings, the extent to which free-text input is allowable, and the implementation of electronic authorship protocols to ensure accountability.
Retool EHR templates, alerts, and interfaces
EHR data input interfaces must work in conjunction with policy changes, and may require an extensive facelift in order to meet user expectations. In order to ease frustrations, save time, maintain healthcare data integrity, and develop an intuitive user experience, organizations may consider implementing a single-sign on system, encouraging the use of standardized responses through drop down menus and check-boxes, adopting back-end technologies such as natural language processing to parse free-text responses, and using a system-wide patient identification protocol to make sure that providers are entering data into the correct chart.
Providers should also be sure that alerts or pop-ups that direct users towards a certain activity are used sparingly. An overwhelming number of these types of interruptions can produce alarm fatigue, cause users to ignore potentially helpful and important warnings, and turn to those counterproductive short-cuts to avoid the annoyances.
Templates should be designed to collect the necessary basic information and all other pertinent data in a standardized manner without limiting users who may need to include patient information that falls outside prescribed responses. “As a physician, I do not want my thinking to be limited in any possible way by a template that I need to fill out in order to create a note,” said Charles McCormick, MD, FAAP, a pediatrician and Associate Medical Director for Health Plan of San Joaquin. “Every patient is different, and not a single one of us fits into the same box.”
Balancing the need for healthcare data integrity with the very real need for flexible, critical thinking is one of the most critical challenges of any EHR optimization project, and must be undertaken with care. Rigidly designed interfaces may produce just as many errors are they hope to prevent, which defeats the purpose of true optimization.
Gather feedback and refine your changes
In that vein, it is important to remember that no human-driven system is static, and no ideal outline of how things should work will ever achieve one hundred percent adherence. EHR technology is intended to be a tool to help providers do their jobs better, not a constant source of stress, distaste, or hatred that makes patient care a chore.
After establishing data governance policies, making technology changes, and rolling out these initiatives to the organization, be sure to field periodic questionnaires or make the rounds to personally check how well the new processes are working.
When something appears to be ineffective, repeat the process of assessing its place in the larger healthcare data integrity effort before committing to any changes. Provide options for additional education and training if staff members seem confused or are repeating the same mistakes.
Above all, be sure that all users understand why adjustments are being made to their workflow and how the changes will ultimately improve the experience and process of patient care. Be open and transparent about data integrity optimization efforts to avoid user resentment, and establish an atmosphere of continuous improvement that welcomes input, makes adjustments when necessary, and keeps the ultimate needs of the patient at the center of all optimization decision-making.