Efficacy of a clinical decision-support system in an HIV practice: a randomized trial.

BACKGROUND: Data to support improved patient outcomes from clinical decision-support systems (CDSSs) are lacking in HIV care. OBJECTIVE: To test the efficacy of a CDSS in improving HIV outcomes in an outpatient clinic. DESIGN: Randomized, controlled trial. (ClinicalTrials.gov registration number: NCT00678600) SETTING: Massachusetts General Hospital HIV Clinic. PARTICIPANTS: HIV care providers and their patients. INTERVENTION: Computer alerts were generated for virologic failure (HIV RNA level >400 copies/mL after a previous HIV RNA level ≤400 copies/mL), evidence of suboptimal follow-up, and 11 abnormal laboratory test results. Providers received interactive computer alerts, facilitating appointment rescheduling and repeated laboratory testing, for half of their patients and static alerts for the other half. MEASUREMENTS: The primary end point was change in CD4 cell count. Other end points included time to clinical event, 6-month suboptimal follow-up, and severe laboratory toxicity. RESULTS: Thirty-three HIV care providers followed 1011 patients with HIV. In the intervention group, the mean increase in CD4 cell count was greater (0.0053 vs. 0.0032 × 109 cells/L per month; difference, 0.0021 × 109 cells/L per month [95% CI, 0.0001 to 0.004]; P = 0.040) and the rate of 6-month suboptimal follow-up was lower (20.6 vs. 30.1 events per 100 patient-years; P = 0.022) than those in the control group. Median time to next scheduled appointment was shorter in the intervention group than in the control group after a suboptimal follow-up alert (1.71 vs. 3.48 months; P < 0.001) and after a toxicity alert (2.79 vs. >6 months; P = 0.072). More than 90% of providers supported adopting the CDSS as part of standard care. LIMITATION: This was a 1-year informatics study conducted at a single hospital subspecialty clinic. CONCLUSION: A CDSS using interactive provider alerts improved CD4 cell counts and clinic follow-up for patients with HIV. Wider implementation of such systems can provide important clinical benefits. PRIMARY FUNDING SOURCE: National Institute of Allergy and Infectious Diseases.

A rheumatology-specific informatics-based application with a disease activity calculator.

OBJECTIVE: To design a rheumatology-specific tool with a disease activity calculator integrated into the electronic medical records (EMRs) at our institution and assess physicians' attitudes toward the use of this tool. METHODS: The Rheumatology OnCall (ROC) application culls rheumatology-pertinent data from our institution's laboratory, microbiology, pathology, radiology, and pharmacy information systems. Attending rheumatologists and rheumatology fellows accessed the ROC and disease activity calculator during outpatient visits at the time of the clinical encounter. RESULTS: During the 12-week study period, 15 physicians used the ROC application and the disease activity calculator during 474 and 429 outpatient clinic visits, respectively. In weekly survey responses, physicians reported that use of the ROC interface improved patient care in 140 (78%) of 179 visits, and that the Disease Activity Score in 28 joints (DAS28) results at the time of the visit would not have changed patient management in 157 (88%) of these, although seeing a trend in DAS28 was useful in 149 (96%) of 156 visits. At the study's conclusion, most physicians reported that the ROC application was useful (11 of 12 physicians) and that seeing a trend in DAS28 improved daily patient care (12 of 13 physicians). CONCLUSION: The ROC application is useful in daily rheumatologic care, and the disease activity calculator facilitates management of patients with rheumatoid arthritis. However, widespread acceptance and use of such tools depend upon the general acceptance of and access to EMRs in the clinical setting. The utility of the disease activity calculator may be limited by the lack of available acute-phase reactant results at the time of the clinical encounter.

Physician ability to assess rheumatoid arthritis disease activity using an electronic medical record-based disease activity calculator.

OBJECTIVE: To assess physicians' concordance with Disease Activity Score in 28 joints (DAS28) categories calculated by an electronic medical record (EMR)-embedded disease activity calculator, as well as attitudes toward this application. METHODS: Fifteen rheumatologists used the EMR-embedded disease activity calculator to predict a rheumatoid arthritis (RA) DAS28 disease activity category at the time of each clinical encounter. RESULTS: Physician-predicted DAS28 disease activity categories ranged from high (>5.1, 15% of cohort, 66 of 429 patient visits) to moderate (>3.2-5.1, 21% of cohort, 90 of 429 patient visits) to low (2.6-3.2, 29% of cohort, 123 of 429 patient visits) to remission (<2.6, 35% of cohort, 150 of 429 patient visits). Overall concordance between calculated DAS28 results and physician-predicted RA disease activity was 64%. Using either the physician-predicted or the calculated DAS28 category as the gold standard, accuracy was greatest for patients in remission (75% and 88% accuracy, respectively) and those with high disease activity (68% and 79% accuracy, respectively), and less for patients with moderate (48% and 62% accuracy, respectively) or low disease activity (62% and 31% accuracy, respectively). CONCLUSION: Accurate physician prediction of DAS28 remission and high disease activity categories, even without immediate availability of the erythrocyte sedimentation rate or the C-reactive protein level at the time of the visit, may be used to guide quantitatively driven outpatient RA management.