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Record W3136122931 · doi:10.1186/s12913-021-06212-x

A feasibility study using time-driven activity-based costing as a management tool for provider cost estimation: lessons from the national TB control program in Zimbabwe in 2018

2021· article· en· W3136122931 on OpenAlexaff
Joconiah Chirenda, Bertha Nhlema Simwaka, Charles Sandy, Katharine Bodnar, Shevone Corbin, Pooja Desai, Tonderai Mapako, Shepherd Shamu, Collins Timire, Ednei Luiz Antônio, A. Makone, Alexander Birikorang, Tawanda Mapuranga, Mkhokheli Ngwenya, T. Masunda, Michael P. Dubé, Eliud Wandwalo, Linden Morrison, Robert S. Kaplan

Bibliographic record

VenueBMC Health Services Research · 2021
Typearticle
Languageen
FieldMedicine
TopicTuberculosis Research and Epidemiology
Canadian institutionsPricewaterhouseCoopers (Canada)
FundersFogarty International CenterWorld Health OrganizationGlobal Fund to Fight AIDS, Tuberculosis and Malaria
KeywordsActivity-based costingHealth administrationMedicineHealth informaticsNursing researchHealth careCost driverHealth economicsCost databaseService (business)Medical emergencyPublic healthOperations managementNursingBusinessMarketingComputer science

Abstract

fetched live from OpenAlex

BACKGROUND: Insufficient cost data and limited capacity constrains the understanding of the actual resources required for effective TB control. This study used process maps and time-driven activity-based costing to document TB service delivery processes. The analysis identified the resources required to sustain TB services in Zimbabwe, as well as several opportunities for more effective and efficient use of available resources. METHODS: A multi-disciplinary team applied time-driven activity-based costing (TDABC) to develop process maps and measure the cost of clinical pathways used for Drug Susceptible TB (DS-TB) at urban polyclinics, rural district and provincial hospitals, and community based targeted screening for TB (Tas4TB). The team performed interviews and observations to collect data on the time taken by health care worker-patient pairs at every stage of the treatment pathway. The personnel's practical capacity and capacity cost rates were calculated on five cost domains. An MS Excel model calculated diagnostic and treatment costs. FINDINGS: Twenty-five stages were identified in the TB care pathway across all health facilities except for community targeted screening for TB. Considerable variations were observed among the facilities in how health care professionals performed client registration, taking of vital signs, treatment follow-up, dispensing medicines and processing samples. The average cost per patient for the entire DS-TB care was USD324 with diagnosis costing USD69 and treatment costing USD255. The average cost for diagnosis and treatment was higher in clinics than in hospitals (USD392 versus USD256). Nurses in clinics were 1.6 time more expensive than in hospitals. The main cost components were personnel (USD130) and laboratory (USD119). Diagnostic cost in Tas4TB was twice that of health facility setting (USD153 vs USD69), with major cost drivers being demand creation (USD89) and sputum specimen transportation (USD5 vs USD3). CONCLUSION: TDABC is a feasible and effective costing and management tool in low-resource settings. The TDABC process maps and treatment costs revealed several opportunities for innovative improvements in the NTP under public health programme settings. Re-engineering laboratory testing processes and synchronising TB treatment follow-up with antiretroviral treatments could produce better and more uniform TB treatments at significantly lower cost in Zimbabwe.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.015
metaresearch head score (Gemma)0.049
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.072
Threshold uncertainty score0.143

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0150.049
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.003
Science and technology studies0.0010.001
Scholarly communication0.0020.003
Open science0.0020.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.221
GPT teacher head0.548
Teacher spread0.327 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations11
Published2021
Admission routes1
Has abstractyes

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