A clinical decision support system is associated with reduced loss to follow-up among patients receiving HIV treatment in Kenya: a cluster randomized trial

Background: Loss to follow-up (LFTU) among HIV patients remains a major obstacle to achieving treatment goals with the risk of failure to achieve viral suppression and thereby increased HIV transmission. Although use of clinical decision support systems (CDSS) has been shown to improve adherence to HIV clinical guidance, to our knowledge, this is among the first studies conducted to show its effect on LTFU in low-resource settings. Methods: We analyzed data from a cluster randomized controlled trial in adults and children (aged ≥ 18 months) who were receiving antiretroviral therapy at 20 HIV clinics in western Kenya between Sept 1, 2012 and Jan 31, 2014. Participating clinics were randomly assigned, via block randomization. Clinics in the control arm had electronic health records (EHR) only while the intervention arm had an EHR with CDSS. The study objectives were to assess the effects of a CDSS, implemented as alerts on an EHR system, on: (1) the proportion of patients that were LTFU, (2) LTFU patients traced and successfully linked back to treatment, and (3) time from enrollment on the study to documentation of LTFU. Results: Among 5901 eligible patients receiving ART, 40.6% (n = 2396) were LTFU during the study period. CDSS was associated with lower LTFU among the patients (Adjusted Odds Ratio—aOR 0.70 (95% CI 0.65–0.77)). The proportions of patients linked back to treatment were 25.8% (95% CI 21.5–25.0) and 30.6% (95% CI 27.9–33.4)) in EHR only and EHR with CDSS sites respectively. CDSS was marginally associated with reduced time from enrollment on the study to first documentation of LTFU (adjusted Hazard Ratio—aHR 0.85 (95% CI 0.78–0.92)). Conclusion: A CDSS can potentially improve quality of care through reduction and early detection of defaulting and LTFU among HIV patients and their re-engagement in care in a resource-limited country. Future research is needed on how CDSS can best be combined with other interventions to reduce LTFU. Trial registration NCT01634802. Registered at www.clinicaltrials.gov on 12-Jul-2012. Registered prospectively.

We conducted a prospective, cluster randomized controlled study in Siaya County, western Kenya to assess the effect of an EHR with CDSS compared to EHR only on timely identification of patients experiencing immunological treatment failure and appropriate action taken [17]. Data collection and follow-up period at each site was 12 months but sites had varying start dates within the study period to allow for facility readiness. At the end of 12 months, each site had achieved the allocated sample size and data collection was stopped. The study was conducted, and reported, in adherence to the CONSORT extension for cluster trials guidelines. In this paper we report on a secondary analysis of the data to assess the effect of a CDSS on LTFU of patients receiving ART at the study sites. Siaya county, where the study was conducted, has one of the highest HIV prevalence in Kenya. Approximately 17.8% of adults aged 15–49 were HIV-positive compared to the national prevalence of 5.6% [10]. The study sites consisted of 20 health facilities where the Kenya Medical Research Institute (KEMRI) provides data management support for routine health service delivery and research. All patients aged two years or older were included in the study. We included all patients who were already receiving ART three months prior to implementation of the EHR at the clinic and during the data collection period but excluded those that were newly initiating treatment after the 9th month of the study since the follow-up time within the study period would only be three months (inadequate time to tell if the patients were LTFU as defined in the MOH Guidelines). Participants had varied follow-up time depending on when they initiated ART at the study site. The Kenyan Ministry of Health’s (MOH) HIV treatment guidelines (adapted from WHO’s HIV consolidated treatment guidelines, 2012) describes a patient that is LTFU as: “a client who has not turned up or come back to the clinic for either a clinical visit or refills for more than 90 days (3 months) from the last scheduled visit” [11, 18]. Before a patient is classified as LTFU, he/she is considered a Defaulter. According to the Kenya MOH guidelines: “A defaulter is a client who has not turned up for either a clinical visit or refills 7 days after their scheduled appointment date [11]. In clinics where paper-based systems are used to document patients treatment records, the daily (or in some cases weekly) appointment list is prepared by manually reviewing individual patient charts and retrieving the date of next visit. At the end of each clinic day, the staff responsible for data management (often data clerks or nurses) review the Daily Attendance Register to identify names of patients who missed their appointments and this is used to classify defaulting patients or those that are LTFU before tracing is initiated through social or community health workers. Timely tracing enables the community health worker to offer the necessary education, counseling and support to the patient and refer them back to the clinic to resume treatment. Of the 20 facilities where KEMRI provides data management support, seven were excluded from the study as they did not have reliable electric power, a secure location for a computer, or permanent data clerks to help with the regular data management activities. Each health facility was considered a cluster due to the uniformity of care offered to the patients. Allocation to study arms was at facility/clinic level and all eligible patients receiving HIV treatment at participating facilities were automatically assigned to the arm of the study to which the clinic was assigned. The KEMRI data management team used block randomization to assign the eligible 13 health facilities into two groups—EHR only (n = 6) or EHR plus CDSS (n = 7), matched by the MOH level and number of patients enrolled on HIV care. Level 2 facility (Dispensary) is defined as: headed by a nurse, offers basic out-patient and some preventive services; Level 3 (Health Center), headed by a clinical officer, offers out-patient, maternal child health and limited in-patient services; Level 4 (District Hospital), headed by a physician, is a district referral facility and offers emergency, outpatient and in-patient services [19]. For each MOH level, whenever a clinic was assigned to the EHR with CDSS group through a random selection, a same-level clinic with comparable number of patients on HIV care was assigned to the EHR-only group. Each group had 1–3 levels of health facilities. Level 1 (Community clinics) were not included since they don’t offer HIV treatment services. The KEMRI data management team were not involved in data analysis and the CDC statisticians who performed the analysis were blinded to the allocation of clinics into the respective arms of the study. In clinics with EHR systems, appointment lists are automatically generated from the computerized system at the start of the clinic day. Lists of defaulters and patients that are LFTU are automatically generated at the end of each week. The 20 HIV clinics in Siaya County where KEMRI supported data management had an EHR system referred to as Comprehensive Care Centre Patient Application Database (C-PAD). The C-PAD EHR was originally developed as a standalone application using Visual Basic for Applications in 2007. It underwent several enhancements and a CDSS was integrated into the 2012 version prior to the start of this study. Following the randomization described above, the intervention group had an EHR with CDSS functionality while the CDSS was turned off (muted) in the control group. The main difference between the two systems is that the version with a CDSS identifies individuals that are LTFU and recommends appropriate action at individual level (included in the patient charts) while the version that is an EHR-only does not make any recommendations beyond generating a weekly list of all patients who missed appointments. Health workers in the sites with EHR and CDSS were trained on the appropriate action to take whenever alerts were encountered. Such action included immediate follow-up of patient or inclusion of a note in the patient chart for action during the next clinic visit. For the two study groups (EHR only and EHR + CDSS), clinicians recorded data on the paper form (the so-called blue card) during the consultation, and the data clerk entered the data into the computer on the same day of clinic visit. For patients in the EHR + CDSS group who miss an appointment and meet the criteria for defaulter or LTFU, the system generates an alert with the patient’s last visit date, date of the missed appointment and number of days since the appointment date and whether they are considered defaulters or LTFU. This information is printed out and included in the individual patient charts with recommendation for appropriate action such as tracing defaulters or revising documentation of status (LTFU, transferred out or dead). The main effect of the intervention is to inform timely tracing of the defaulting patients or those that are LTFU. In the EHR only (usual care) group, the alerts were turned off in the instance of the EHR installed and there were no individual patient level alerts printed out nor recommendations filed in the patient charts; the clinical staff relied on weekly summary reports which list all patients who missed appointments in order to make decisions on follow-up. KEMRI data managers routinely reviewed the data and any missing or unusual values were sent back to the clinician via the data clerks for completion, correction or confirmation. The primary outcome measure for this study was the proportion of patients receiving HIV treatment that were LTFU at least once during the study period. Secondary outcomes measures were the proportion of LTFU patients traced and successfully linked back to treatment within the study period, and time from enrollment on the study to documentation of LTFU. The KEMRI data management team abstracted selected variables from the EHR. Individual patient records were de-identified and assigned study numbers that could not be traced back to the patient. Analytic datasets were created and duplicate entries deleted. Such duplicate entries may have resulted from erroneous creation of new records for patients who could not be correctly identified at the registration desk during clinic visits but were eventually correctly matched and linked to previous visits. Patients were coded as LFTU if they met the MOH’s definition. Those that were LTFU but were traced and referred back to the facility and successfully re-initiated treatment were still counted as LTFU. Approximately 15% of patients that were LTFU, were lost more than once during the study period and the proportions were comparable across the groups. We excluded from analysis, records of patients who only had one documented clinic visit during the study period to ensure that transit patients visiting the clinic for drugs refill only or had not made up their minds about permanently enrolling on care at the health facility were not mistakenly counted as LTFU. The sample size calculation was adapted from the method used in the main study reported in [17]. We calculated means with 95% confidence intervals and medians with inter-quartile ranges to summarize continuous variables. We used the Kruskal–Wallis test to compare distribution of medians and ANOVA to test for mean differences by outcome status. We used generalized estimating equations to analyze clustered data to determine predictors of LTFU over time and Cox proportional hazard regression to identify risk factors associated with time to documentation of first loss to follow-up. We used Kaplan–Meier survival plots and obtained hazard ratios from the clustered Cox regression to estimate the effect size of the intervention on the time-to-event outcomes and reported on the corresponding p values. Data were censored at the last follow-up visit. The multi-variable analysis was adjusted for the following patient-level covariates: age, sex, marital status, CD-4 category, WHO stage, and treatment regimen; and site-level variable (level of health facility). We used Stata (version 14.0) [Stata Corp, Austin, Texas] and Statistical Analysis Software (SAS® 9.4 Base SAS. Cary, NC: SAS Institute Inc., 2014) for both data management and statistical analysis. The data contained missing values for some of the patient-level covariates. We compared the results from a complete case analysis (CCA) and multiple imputation (MI) and selected the MI method for our analysis as it reduces bias and provides more efficient inferences since we could not tell with certainty that data were missing completely at random (MCAR). The Markov chain Monte Carlo method was used to impute missing data. In the logistic regression model, variables with a high proportion of missing data (> 30%) were dropped from the analysis. The study was reviewed in accordance with the Centers for Disease Control and Prevention (CDC) human research protection procedures and was determined to be research, but CDC investigators did not interact with human subjects or have access to identifiable data or specimens for research purposes. The Kenya Medical Research Institute’s (KEMRI) Ethical Review Committee reviewed and approved the study. All data were de-identified by the KEMRI staff participating in this study prior to analysis. This trial is registered with ClinicalTrials.gov, number {“type”:”clinical-trial”,”attrs”:{“text”:”NCT01634802″,”term_id”:”NCT01634802″}}NCT01634802.

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• Dima Health