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Related Concept Videos

Introduction to Statistical Process Control01:15

Introduction to Statistical Process Control

Statistical Process Control (SPC) is a method used to monitor and control quality within processes, particularly in manufacturing and service delivery, by employing statistical methods. SPC aims to distinguish between natural (common cause) variation and variation due to specific changes or events (special cause), allowing for timely improvements and sustained quality. The control chart, a pivotal tool in SPC, visually displays data over time alongside a central line of upper and lower control...
Quality Assurance01:19

Quality Assurance

Quality assurance is the overarching term used to describe the activities employed to ensure the proper performance of a system. These activities can be classified into three categories: quality control, quality assessment, and internal corrective measures. Typically, these activities work cyclically: quality control is performed before and during the analysis, while quality assessment occurs during and after the investigation. Internal corrective measures are implemented based on the findings...
Quality Control01:05

Quality Control

Quality control is one of the three cyclical quality assurance activities that help keep a system under statistical control. Typical quality control activities include creating quality control charts, conducting proficiency testing, and documenting and archiving results.
Quality control helps track data, visualize trends, and identify variations, making it easier to detect deviations that may affect the accuracy of an analysis. One way to do this is by generating a quality control chart, which...
Overview of Minitab01:11

Overview of Minitab

Minitab is a statistical software package designed for data analysis. With its origins in the 1970s and development at Pennsylvania State University, Minitab has grown significantly in its capabilities and applications. It plays a crucial role in quality management projects, especially in Six Sigma initiatives, by offering tools for process improvement and statistical analysis. Minitab's significance lies in its user-friendly interface, making complex statistical analysis accessible to users...
Interpreting Run Charts01:25

Interpreting Run Charts

Run charts, essentially line graphs plotted over time, serve as fundamental yet effective tools for process analysis. They chronicle data sequentially, facilitating the identification of trends, shifts, or cyclical movements. This graphical representation is instrumental in determining whether a process is stable or exhibits signs of potential instability indicative of special cause variation. In the healthcare domain, run charts depict infection rates over time, enabling hospitals to monitor...
Run Charts01:12

Run Charts

Run charts serve as an essential instrument for visualizing the performance of various processes over time, enabling the identification of trends and patterns crucial for quality improvement. These charts map out a series of data points chronologically, offering insights into the stability and efficiency of a process. A run chart's creation involves plotting data points on a graph, with the time intervals on the horizontal axis and the specific measurements on the vertical axis. For example,...

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Related Experiment Video

Updated: May 24, 2026

Simulation of a Scaled Assembly Process with Collaboration of a Robotic Arm and Monitoring through a Vision System for Quality Control
05:47

Simulation of a Scaled Assembly Process with Collaboration of a Robotic Arm and Monitoring through a Vision System for Quality Control

Published on: August 29, 2025

Process Mining for Quality Improvement in Malawi.

Dumisani Nkhoma1, Matthias John2, Usman Iqbal1

  • 1Institute of Evidence Based Healthcare, Faculty of Health Sciences and Medicine, Bond University, Robina, QLD, Australia.

Studies in Health Technology and Informatics
|May 23, 2026
PubMed
Summary
This summary is machine-generated.

Process mining offers an objective, data-driven approach to improve non-communicable disease (NCD) program management in Malawi. This method enhances quality improvement by analyzing patient data to identify and resolve clinic workflow inefficiencies, boosting attendance and treatment adherence.

Keywords:
Malawihealthcare efficiencymixed-methods designnon-communicable diseasesprocess miningquality improvement

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Project-Based Learning Guidelines for Health Sciences Students: An Analysis with Data Mining and Qualitative Techniques
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Related Experiment Videos

Last Updated: May 24, 2026

Simulation of a Scaled Assembly Process with Collaboration of a Robotic Arm and Monitoring through a Vision System for Quality Control
05:47

Simulation of a Scaled Assembly Process with Collaboration of a Robotic Arm and Monitoring through a Vision System for Quality Control

Published on: August 29, 2025

Project-Based Learning Guidelines for Health Sciences Students: An Analysis with Data Mining and Qualitative Techniques
13:44

Project-Based Learning Guidelines for Health Sciences Students: An Analysis with Data Mining and Qualitative Techniques

Published on: December 9, 2022

Area of Science:

  • Health Services Research
  • Health Informatics
  • Process Mining Applications

Background:

  • Long waiting times in Malawi's non-communicable disease (NCD) programs lead to poor clinic attendance and treatment default.
  • Existing quality improvement (QI) practices, like root-cause analysis, are often subjective and lack data-driven insights.

Purpose of the Study:

  • To introduce and describe the first protocol for implementing process mining (PM) in Malawi's healthcare system for NCD program management.
  • To demonstrate the feasibility and utility of PM as an objective, data-driven approach to complement existing QI methods.
  • To enhance clinical efficiency and quality improvement initiatives within Malawi's NCD program.

Main Methods:

  • Sequential mixed-methods design combining qualitative (focus group discussions, key-informant interviews, document review) and quantitative process mining.
  • Qualitative data analysis using thematic analysis to identify perceived workflow bottlenecks.
  • Quantitative process mining involving data extraction, pre-processing, clustering, mining, and visualization using algorithms like Heuristic or Fuzzy miner, presented as Business Process Model and Notation (BPMN) nets.

Main Results:

  • The study protocol outlines the systematic implementation of process mining in the Malawian healthcare context.
  • Process mining is expected to reveal process variations and inefficiencies in NCD patient care pathways.
  • The integration of PM is anticipated to provide a scalable tool for evidence-based monitoring of healthcare processes.

Conclusions:

  • Process mining offers a valuable, objective, and data-driven complement to traditional quality improvement methods in NCD program management.
  • Implementing process mining in Malawi's NCD program can enhance clinical efficiency, improve patient attendance, and reduce treatment default.
  • With the rise of electronic health records, process mining presents a scalable solution for monitoring and improving healthcare quality.