Everyone is familiar with evidence based protocols (EBPs), the research-based guidelines or procedures used by clinicians to select and guide the course of individual treatment. EBPs originated to offer reliable guidance to clinicians, without the need to track ever-growing amounts of research data and patient findings. Clinical decision support (CDS) systems provide a means of applying guidance like this more globally and systemically, ideally resulting in more consistent, high-quality care. CDS functionality built into many EHR systems typically enables organizations to select and install knowledge bases (i.e., drug allergies/interactions) as well as “rule sets” (vetted treatment protocols, local treatment standards, etc.).
In operation, CDS systems process physician or clinician inputs and patient data according to rule sets and knowledge bases installed in the EHR system. Then, they communicate information that, according to the rules, is designated as important to clinical decision making. CDS functionality is very versatile and flexible. Organizations have found that they can develop rule sets for many purposes. For example, CDS can serve practical and administrative needs with rules that help ensure all data, assessments, and documentation required by a payer for a specific patient diagnosis are completed at the time a claim is filed.
Clinical leaders can modify decision rules to ensure that new assessment criteria or treatment steps are used for all new patients with a particular diagnosis. For prescribers, it means e-prescriptions will automatically be checked for possible contraindications, allergies, or interactions. To an authorizations team, it might mean creating reminders to clinicians to complete evaluations needed to support treatment authorization or reauthorization. Of course, the “holy grail” of CDS is about improving the quality of patient care by providing physicians and clinical personnel with more personalized, symptom-specific diagnostic and treatment information.
Ideally, CDS systems are seen as critical components in creating EHR-based “learning systems” that continually incorporate clinical advances, apply them to ever-growing and more detailed patient databases, and increase clinicians’ abilities to predict-based on patient data and demographics-which of a range of interventions will offer the best outcome.
While many medical provider groups have made progress in developing CDS rule sets that improve care quality for patients, such definitive rules for behavioral healthcare have proven far more difficult to develop. The reason, simply put, is a lack of data to stand behind them.
Deo Garlock, Director of Behavioral Health Informatics at Duke University Health System (Durham, N.C.), agrees. He says that current best practices and treatment protocols are based on data that are sometimes decades old, generated in studies that serve too many widely varying objectives-everything from clinical trials to public health.
“The data for treatment guidelines are generally too old and generic to effectively apply to an individual patient," he explains. "Determining probability of treatment outcomes is difficult and requires a lot of data and sophisticated statistical analyses.”