Effect of a clinical decision support system on early action on immunological treatment failure in patients with HIV in Kenya: a cluster randomised controlled trial

Abstract

Summary Background A clinical decision support system (CDSS) is a computer program that applies a set of rules to data stored in electronic health records to offer actionable recommendations. We aimed to establish whether a CDSS that supports detection of immunological treatment failure among patients with HIV taking antiretroviral therapy (ART) would improve appropriate and timely action. Methods We did this prospective, cluster randomised controlled trial in adults and children (aged ≥18 months) who were eligible for, and receiving, ART at HIV clinics in Siaya County, western Kenya. Health facilities were randomly assigned (1:1), via block randomisation (block size of two) with a computer-generated random number sequence, to use electronic health records either alone (control) or with CDSS (intervention). Facilities were matched by type and by number of patients enrolled in HIV care. The primary outcome measure was the difference between groups in the proportion of patients who experienced immunological treatment failure and had a documented clinical action. We used generalised linear mixed models with random effects to analyse clustered data. This trial is registered with ClinicalTrials.gov, number NCT01634802. Findings Between Sept 1, 2012, and Jan 31, 2014, 13 clinics, comprising 41 062 patients, were randomly assigned to the control group (n=6) or the intervention group (n=7). Data collection at each site took 12 months. Among patients eligible for ART, 10 358 (99%) of 10 478 patients were receiving ART at control sites and 10 991 (99%) of 11 028 patients were receiving ART at intervention sites. Of these patients, 1125 (11%) in the control group and 1342 (12%) in the intervention group had immunological treatment failure, of whom 332 (30%) and 727 (54%), respectively, received appropriate action. The likelihood of clinicians taking appropriate action on treatment failure was higher with CDSS alerts than with no decision support system (adjusted odds ratio 3.18, 95% CI 1.02–9.87). Interpretation CDSS significantly improved the likelihood of appropriate and timely action on immunological treatment failure. We expect our findings will be generalisable to virological monitoring of patients with HIV receiving ART once countries implement the 2015 WHO recommendation to scale up viral load monitoring. Funding US President’s Emergency Plan for AIDS Relief (PEPFAR), through the US Centers for Disease Control and Prevention.

%svy_logistic_regression: A generic SAS macro for simple and multiple logistic regression and creating quality publication-ready tables using survey or non-survey data

“…Results from simple and multiple regression can also be presented side-by-side in one table. Some examples of publications that adopt this convention of presenting regression results are provided in the references [12–15]. In order to accomplish this, one has to manually copy different parts of output into a template.…”

‘%svy_logistic_regression: A generic SAS® macro for simple and multiple logistic regression and creating quality publication-ready tables using survey or non-survey data

“…Results from simple and multiple regression can also be presented side-by-side in one table. Some examples of publications which adopt this convention of presenting regression results are provided in the references (12–15). In order to accomplish this, one has to manually copy different parts of output into a template.…”

Implementing an Open Source Electronic Health Record System in Kenyan Health Care Facilities: Case Study (Preprint)

“…For example, in Kenya, EHRs have been used within projects that mainly support HIV care leading to well-developed systems for this disease area. For the management of both HIV and tuberculosis (TB), the result of digitization has been better record-keeping, patient management, follow-up, and stock control [11][12][13][14]. Although these implementations were largely successful, challenges encountered included limited interoperability with other systems and a lack of direct use by clinicians-systems are often used by clerks who enter data on behalf of the clinical team [15].…”