Evidence map›Paper›PMID 40102889›Full record

ArticleBMC health services research2025

A web-based platform for optimizing healthcare resource allocation and workload management using agile methodology and WISN theory.

Akash Gajanan Prabhune, P S Karpaga Priya, Rohit Chandra, Ankur Thakur, Viany R Srihari, Sachin S Bhat

Abstract read
In one paragraph

Article in BMC health services research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. Article
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

6 authors.

Akash Gajanan PrabhuneADMIRE Centre for Advancing Digital Health, Institute of Health Management Research, Bangalore (IIHMR-B), Bengaluru, India.ORCID http://orcid.org/0000-0002-3046-9067
P S Karpaga PriyaADMIRE Centre for Advancing Digital Health, Institute of Health Management Research, Bangalore (IIHMR-B), Bengaluru, India. karpaga.p@iihmrbangalore.edu.in.ORCID http://orcid.org/0000-0002-1414-4764
Rohit ChandraADMIRE Centre for Advancing Digital Health, Institute of Health Management Research, Bangalore (IIHMR-B), Bengaluru, India.
Ankur ThakurADMIRE Centre for Advancing Digital Health, Institute of Health Management Research, Bangalore (IIHMR-B), Bengaluru, India.
Viany R SrihariADMIRE Centre for Advancing Digital Health, Institute of Health Management Research, Bangalore (IIHMR-B), Bengaluru, India.ORCID http://orcid.org/0009-0005-3497-0595
Sachin S BhatADMIRE Centre for Advancing Digital Health, Institute of Health Management Research, Bangalore (IIHMR-B), Bengaluru, India.ORCID http://orcid.org/0000-0002-4001-8411

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundEffective healthcare workforce management is critical for ensuring quality care delivery, particularly in resource-constrained settings. The World Health Organization's (WHO) Workload Indicators of Staffing Need (WISN) methodology provides an evidence-based framework for optimizing staffing levels. However, manual implementation of the WISN methodology is labour-intensive, error-prone, and time-consuming. To address these challenges, the Platform for Resource Allocation and Optimization for Healthcare Facilities (PRAYOJN) platform was developed as a web-based tool to automate WISN calculations, streamline data analysis, and improve workforce planning.

objectiveTo develop and validate a web-based system that automates the WISN methodology for healthcare workforce planning.

methodsThe PRAYOJN platform was developed using an agile methodology, structured over five iterative sprints. These sprints incorporated stakeholder feedback to refine system functionalities, ensuring adaptability to real-world healthcare needs. The platform integrates data for principal, supporting, and ancillary tasks to calculate staffing requirements. Key functionalities include automated computation of Available Work Time (AWT), Standard Workload (SW), Category Allowance Factor (CAF), and Individual Allowance Factor (IAF). Alpha testing validated usability and accuracy, while beta testing in a clinical phlebotomy department assessed real-world performance.

resultsThe platform calculated an ideal staffing requirement of 15.53 Full-Time Equivalent (FTE) for the phlebotomy department, aligning closely with the current staff strength of 15 FTE. Agile development ensured iterative improvements, enhancing user interface (UI) and user experience (UX). Feedback highlighted the platform's user-friendly design, with dynamic visualizations such as pie charts and bar graphs aiding workload interpretation. Users praised its efficiency, adaptability, and role in reducing calculation complexity.

conclusionPRAYOJN modernizes and enhances WISN-based workforce planning by automating workload calculations, improving data visualization, and supporting real-time decision-making. Its scalability and intuitive interface position it as a valuable tool for optimizing staffing efficiency across diverse healthcare environments.

Indexed as

InternetPersonnel Staffing and SchedulingResource AllocationWorkloadHumansAgile development in healthcareHealthcare resource allocationHealthcare workforce optimizationPRAYOJN platformStaffing automationWorkforce planning toolWorkload indicators of staffing need (WISN)

Identifiers

PMID40102889
PMCPMC11916971

What OpenQuestion holds

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LicenceCC BY-NC-ND
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Registered trials

None linked

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.