Evidence map›Paper›PMID 31018846›Full record

ArticleBMC health services research2019

Operational characteristics of antiretroviral therapy clinics in Zambia: a time and motion analysis.

Radhika P Tampi, Taniya Tembo, Mpande Mukumba-Mwenechanya, Anjali Sharma, David W Dowdy, Charles B Holmes, Carolyn Bolton-Moore, Izukanji Sikazwe, Austin Tucker, Hojoon Sohn

Abstract read
In one paragraph

Article in BMC health services research, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

9 citing papers in PubMed.

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  6. Costs along the TB diagnostic pathway in Uganda.The international journal of tuberculosis and lung disease : the official journal of the International Union against Tuberculosis and Lung Disease · 2021
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors.

Radhika P TampiDepartment of Epidemiology, Johns Hopkins Bloomberg School of Public Health, 615 N. Wolfe Street, Baltimore, MD, 21205, USA.
Taniya TemboCenters for Infectious Disease Research (CIDRZ), Lusaka, Zambia.
Mpande Mukumba-MwenechanyaCenters for Infectious Disease Research (CIDRZ), Lusaka, Zambia.
Anjali SharmaCenters for Infectious Disease Research (CIDRZ), Lusaka, Zambia.
David W DowdyDepartment of Epidemiology, Johns Hopkins Bloomberg School of Public Health, 615 N. Wolfe Street, Baltimore, MD, 21205, USA.
Charles B HolmesCenters for Infectious Disease Research (CIDRZ), Lusaka, Zambia.
Carolyn Bolton-MooreCenters for Infectious Disease Research (CIDRZ), Lusaka, Zambia.
Izukanji SikazweCenters for Infectious Disease Research (CIDRZ), Lusaka, Zambia.
Austin TuckerDepartment of Epidemiology, Johns Hopkins Bloomberg School of Public Health, 615 N. Wolfe Street, Baltimore, MD, 21205, USA.
Hojoon SohnDepartment of Epidemiology, Johns Hopkins Bloomberg School of Public Health, 615 N. Wolfe Street, Baltimore, MD, 21205, USA. hsohn6@jhu.edu.ORCID http://orcid.org/0000-0001-5837-3844

Funding

Bill and Melinda Gates Foundation OPP1115306
6 · The paper itself

Abstract

backgroundThe mass scale-up of antiretroviral therapy (ART) in Zambia has taken place in the context of limited infrastructure and human resources resulting in many operational side-effects. In this study, we aimed to empirically measure current workload of ART clinic staff and patient wait times and service utilization.

methodsWe conducted time and motion (TAM) studies from both the healthcare worker (HCW) and patient perspectives at 10 ART clinics throughout Zambia. Trained personnel recorded times for consecutive discrete activities based on direct observation of clinical and non-clinical activities performed by counselors, clinical officers, nurses, and pharmacy technicians. For patient TAM, we recruited consenting patients and recorded times of arrival and departure and major ART services utilized. Data from 10 clinics were pooled to evaluate median time per patient spent for each activity and patient duration of stay in the clinic.

resultsThe percentage of observed clinical time for direct patient interaction (median time per patient encounter) was 43.1% for ART counselors (4 min, interquartile range [IQR] 2-7), 46.1% for nurses (3 min, IQR 2-4), 57.2% for pharmacy technicians (2 min, IQR 1-2), and 78.5% for clinical officers (3 min, IQR 2-5). Patient workloads for HCWs were heaviest between 8 AM and 12 PM with few clinical activities observed after 2 PM. The length of patient visits was inversely associated with arrival time - patients arriving prior to 8 AM spent 61% longer at the clinic than those arriving after 8 AM (277 vs. 171 min). Overall, patients spent 219 min on average for non-clinical visits, and 244 min for clinical visits, but this difference was not significant in rural clinics. In comparison, total time patients spent directly with clinic staff were 9 and 12 min on average for non-clinical and clinical visits.

conclusionCurrent Zambian ART clinic operations include substantial inefficiencies for both patients and HCWs, with workloads heavily concentrated in the first few hours of clinic opening, limiting HCW and patient interaction time. Use of a differentiated care model may help to redistribute workloads during operational hours and prevent backlogs of patients waiting for hours before clinic opening, which may substantially improve ART delivery in the Zambian context.

Indexed as

Health PersonnelTime and Motion StudiesAmbulatory Care FacilitiesAnti-Retroviral AgentsHIV InfectionsHumansRural Health ServicesWorkloadZambiaAnti-Retroviral AgentsAllocation of resourcesAntiretroviral therapy care evaluationAntiretroviral therapy program monitoringProgram efficiencyTime and motion studiesWorkload

Identifiers

PMID31018846
PMCPMC6480736

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LicenceCC BY
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.