Decision support includes many different elements that may need to be considered, involving the entire scope of a patient’s care process – not just what the patient is currently being treated for, but also co-morbidities, family history, primary care, social support, available resources and financial considerations. Bringing together all the elements of patient information into one system eliminates flipping between systems and Post-It-Note-type records and documentation.
Meaningful use compels organizations to integrate data into one usable information system, and in doing so, centralizes the information for decision support. Only with this complete portfolio of information can decision-support systems be leveraged to provide a full complement of available options for treatment at the point of care.
While meaningful use is viewed by many as merely a means to secure financial incentives, its benefits go well beyond financial. Meaningful-use requirements force organizations to ensure their systems are compliant, which supports a strategy to comply with the first wave of the federal pay-for-performance programs, such as value-based purchasing.
One of the most important benefits of meaningful use is that it has led organizations to configure their healthcare information systems (HIS) with sets of reminders/checks for those things that cannot always be instantly recalled. Just like other industries, these reminders/checks need to be in place to incorporate regulations and data as well as updates, new theories and new methods into a usable format.
Meaningful use specifically requires use of clinical decision support to comply. While there are many methods of managing the process, let’s apply the example of the iterative, four-step management method for the continuous improvement of processes known as PDCA, or “Plan. Do. Check. Adjust.”
Plan. This step is where the organization decides how the decision-support system based on meaningful-use requirements will look. The organization needs to take a broad view of high-volume/high-cost diagnoses and procedures and target which ones will provide the most benefit as initial areas for decision support. Integrating these areas with other quality initiatives should be strongly considered.
Do. In this, the design phase, the organization determines content and process for decision support. There is no need to re-invent the content wheel since there are many evidence-based resources providing algorithms for care. However, it is important to analyze these algorithms with clinicians since they are best suited to determine what data should be presented. Even with evidence-based best practices, it is vital to incorporate clinician input. A working group with clinicians and clinical informatics staff can build workflow compatibility into system design.
Check. In the measurement and testing phase, clinicians test the system for usability and accuracy. Checking allows revisions to be made (in the next step of this cycle) based upon feedback during this phase of PDCA. This process is iterative, especially in the “C” and “A” phases. The system should then be tested again, and ideally new clinical testers engaged for additional input.
Adjust. As each test is completed, adjustments need to be made in a timely manner. Maintaining momentum is important and will ultimately accrue to adoption. With competing initiatives, IT staff can often be pulled off other projects, resulting in a lack of momentum and waning clinician interest.
The purpose of meaningful use is to guide care providers to deliver best-practice care – the key word here being “guide,” because information provided does not dictate care (make decisions), but rather helps those making the decisions to be as well informed as possible.
Optimizing inputs with better decision support should lead to more intelligent decision making, more appropriate patient care and more desirable outcomes.
About the author
Dr. Anita Karcz is chief medical officer and
co-founder of the Institute for Health Metrics (IHM). For more on IHM, click here.
