Recommendations have already been made for accelerating the development and adoption of a computerized decision support system (CDSS) for evidence-based medicine (Sim et al., 2001
). By using novel health information technology, researchers can bring guideline information to the point where decisions are being made (Deering 2002
; Tierney 2001
Some attempts, in various medical fields, have been made to implement algorithms via computer systems. Implementation, acceptance, and adherence are generally more successful in settings where computerized systems, such as computerized medical record systems or computerized physician orders, are already in place. One review of computer-based CDSSs concluded that these systems are increasing rapidly, and their quality is improving (Hunt et al., 1998
). The authors go on to say that these algorithms “can enhance clinical performance for drug dosing, preventive care and other aspects of medical care.” A more recent systematic review examined controlled trials assessing the effects of computerized clinical decision support systems (CDSSs) (Garg et al., 2005
). Of the 100 trials using a CDSS, many improved clinician performance, but effects on patient outcomes were inconsistent and understudied. The authors did find further evidence, however, that studies in which users were automatically prompted reported better performance than those in which users had to actively initiate the system.
Further validation for employing a computerized, clinical decision making system can be found by considering the conclusions drawn in a study by Tierney and colleagues (Tierney 2001
). They noted that guidelines regarding patient care were more likely to be followed by the physician when they were presented while the patient was at the clinic, rather than afterwards. Physicians were required to make a response to follow or not follow each computer suggestion, a process which not only assures that the physician actually considers information/recommendation presented, but also that their clinical judgment remains primary in determining patient care. This study models the dual task theory, which stresses the importance of timing in relaying critical information to physicians (Buff et al., 1986
). The Tierney study is an exception. Most current strategies for shaping physician behavior fall short of their desired outcome. Our computer platform lends itself to provide instant consultation during the care of patients with MDD.
5.1 Barriers to Implementation of a Computerized Decision Support System
Despite the advantages, significant barriers continue in terms of implementing computerized guidelines. Key issues identified to date include the relevance and accuracy of the messages, as well as flexibility (Rousseau et al., 2003
). In a recent review of 70 trials using clinical decision support systems, 75% of interventions succeeded when decision support was provided to clinicians automatically, whereas none succeeded when clinicians were required to seek out the advice of the decision support system (Kawamoto et al., 2005
), suggesting effective clinical decision support systems must provide decision support automatically as part of the clinician's workflow, deliver decision support at the time and location of the decision making, provide actionable recommendations, and use a computer to generate the decision support.
It is clear that the central issue is how to incorporate efficacious treatments in real-world clinical settings, such that they will be utilized and remain a part of the system of care. Physician adherence is critical in translating recommendations into improved care. As seen in , other barriers to automated feedback systems have already been identified – including physician resistance. Other examples of barriers seen in practice to date with automated feedback systems include varying degrees of computer literacy, lack of technical support, and patient-related factors. These barriers emphasize that to be successful a computerized decision support system must be easy to use and feasible for use in real-world practice settings. In the next section, we discuss how a computerized decision support system developed by our group potentially addresses these concerns.
Barriers to Automated Feedback Systems in Practice
5.2 Description of the CDSS-D
Based on the problems discussed, we have developed a computerized clinical support system for depression (CDSS-D) for use by physicians to enhance their ability to provide the best evidence-based treatment for depression (Trivedi et al., 2000
; Trivedi et al., 2002
; Trivedi et al., 2004a
). Given the wide variation in computer literacy and lack of technical support, we have designed the CDSS-D so that it is user-friendly, with only five main screens to be navigated during a normal follow-up clinical visit. This technology incorporates the most current information about the treatment of depression, as well as providing an easy-to-use interface allowing physicians to use this decision support tool within the context of a routine clinical visit. CDSSs, as opposed to a paper-and-pencil format, have the advantage of allowing the practicing physician to have timely and efficient access to information at the time of treatment decisions
, as well as the added benefit of integrating new findings into the decision support system immediately.
In addition, our CDSS-D provides all the prompts necessary to implement the algorithm without the need of additional personnel. In contrast to a paper-and-pencil format, the computerized algorithm should facilitate the process of following the suggested dosing schedules and tactical recommendations by displaying the recommended dosage and treatment options at that point in time according to the decision rules. Additionally, all patient information, medication information, medication dosages, next appointments, and progress notes are accessible with a click of the computer mouse and recorded electronically, thereby reducing time-consuming paperwork for both physicians and nonphysician staff. The program also provides a recommended time frame for the patient to return based on algorithm stage. By suggesting the next appointment automatically via computer, the CDSS-D should assure that the appropriate time between visits is achieved. The most significant advantage is that feedback from the CDSS-D is ongoing and available during visits rather than before or after the visits.
In terms of feasibility in real-world clinical settings, we are currently comparing the effectiveness and feasibility of a computerized depression algorithm compared to the paper-and-pencil format and usual care in an ongoing, large, multisite NIMH-funded study (5R01MH064062-2 (Computerized Decision Support System for Depression
) in public mental health tertiary care settings (Trivedi et al., 2007
). This study will examine the effect of the CDSS-D on physician adherence to the algorithm, as well as on patient outcomes compared to the paper-and-pencil format and usual care.
shows the treatment evaluation screen seen by the clinician when using the CDSS-D. As the name suggests, this is the stage at which the clinician addresses the issues of the patient's level of depressive symptoms, adherence to medication, and side-effect burden at that point in time. The data entered into the computer are used to drive the clinical decision, thereby facilitating measurement-based care. This screen also provides information regarding the treatment to date – with both a graphical presentation of progress and a copy of the last visit note available. As can be seen, the CDSS-D has been designed to be user-friendly and easy to navigate.
A Sample Computerized Decision Support System for Depression Enabling Physician Decision Making Based on the Symptom Severity and Tolerability of Medication
In much the same way as STAR*D implementation was described in section 3.2, an operationalized treatment algorithm based on TMAP is used by the CDSS-D program. To ensure that medication was optimally used in terms of dose and duration, the program uses preidentified critical decision points (weeks 4, 6, 8, 10, and 12) for each medication stage. As with the STAR*D algorithm, at each critical decision point changes in either the treatment strategy or tactic are recommended based on the current dose and duration of the particular medication together with the degree of symptom change and side-effect burden. The clinician, using the decision rules provided, uses this data (obtained as part of measurement-based care) to drive clinical treatment at that point in time, thereby tailoring treatment to the individual patient. For example if after 6 weeks, a patient has shown minimal or no response to a subtherapeutic dose of sertraline (e.g. 50mg QD) but is tolerating the medication, then the decision support function would recommend that the dose of the antidepressant be increased. As stated later, this strategy has implications not only for other psychiatric disorders, but also for general medical disorders. Once a treatment guideline is established it can be programmed into the computer in a similar fashion as used in the CDSS-D.