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

Abstract

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.

Conflict of interest statement

Potential conflicts of interest: no conflicts.

Figures

Figure 1. Flow diagram of providers and patients in the study

The EMR tracks patient case status as active or inactive (loss to follow-up, transferred medical care, or deceased). The patients of participating providers were randomized to the interactive alert intervention arm or the static alert control arm. There was no statistical difference in mortality or lost to follow-up case status, but the number of patients who transferred medical care (“left practice”) during the study was greater in the intervention than the control arm (1.2% versus 5.1% patients, p

Figure 2. Flow diagram of interactive and static computer alerts

See Methods and Appendix for details.

Figure 3

Mean (95% CI) change in CD4 count per month by providers with evaluable patients in both arms.

Figure 4

Kaplan-Meier analysis of time-to-next scheduled appointment following the first suboptimal follow-up (SOF) and first toxicity (TOX) alerts.