Challenges of data quality for the management of prostate cancer in Tanzania: A hurdle towards improving the quality of service

Data quality in clinical notes is crucial for providing quality prostate cancer services, especially in countries with weak health systems and a growing burden of non-communicable diseases like prostate cancer. Poor data quality can lead to inefficient services, delayed treatment, and compromised patient outcomes. The objective of this study was to examine the quality of clinical notes for prostate cancer patients treated at five tertiary hospitals in Tanzania from January to December 2022. A sequential mixed-method approach was employed, combining quantitative and qualitative data collection. Patients' level information from electronic and manual clinical notes were extracted and reviewed. In-depth interviews were conducted among 25 healthcare providers to explore data quality challenges in clinical notes. Quantitative data analysis used descriptive analysis via SPSS 27 software to determine data completeness and accuracy. In-depth interviews were conducted among 25 healthcare providers to explore data quality challenges. Qualitative data analysis utilized thematic analysis with hybrid inductive and deductive reasoning via NVivo 14 software. Clinical notes for prostate cancer management had poor quality. While the overall accuracy of documented variables was high at 1,494 (99.4%), documentation of key clinical variables was low; specifically, the clinical stage was documented in only 1,052 (70.0%) clinical notes, and the Gleason score in 923 (61.4%). Age, clinical presentation, and type of treatment showed high completeness. Themes established included lack of knowledge on data quality, poor integration of data, shortage of human resources for health, lack of supervision and the coexistence of electronic and manual record systems. The Health Information Management System and hospital-based cancer registries were not integrated. Documentation rates for clinical stage and Gleason score in the clinical notes of patients with prostate cancer were low in Tanzania. Improving the quality of clinical notes requires collaborative efforts among stakeholders focusing on structural reforms and capacity building of personnel.

Authors

Institutions

Publication Details

Journal
PLOS Global Public Health
Published
2025-12-04
DOI
https://doi.org/10.1371/journal.pgph.0005579
Primary Topic
Data Quality and Management
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Challenges of data quality for the management of prostate cancer in Tanzania: A hurdle towards improving the quality of service

Daudi Simba, Castory Munishi, Mark Mseti, Obadia Venance Nyongole et al.
PLOS Global Public Health
Data Quality and Management
article

Challenges of data quality for the management of prostate cancer in Tanzania: A hurdle towards improving the quality of service

Daudi Simba, Castory Munishi, Mark Mseti, Obadia Venance Nyongole, Nathanael Sirili, Gasto Frumence, David Urassa, Raphael Zozimus Sangeda, Bruno Sunguya, Bartholomeo Nicholaus, Frank Kiwara, Njiku Kimu, Frank Chacha, Emmanuel Lugina
article en

Abstract

Data quality in clinical notes is crucial for providing quality prostate cancer services, especially in countries with weak health systems and a growing burden of non-communicable diseases like prostate cancer. Poor data quality can lead to inefficient services, delayed treatment, and compromised patient outcomes. The objective of this study was to examine the quality of clinical notes for prostate cancer patients treated at five tertiary hospitals in Tanzania from January to December 2022. A sequential mixed-method approach was employed, combining quantitative and qualitative data collection. Patients' level information from electronic and manual clinical notes were extracted and reviewed. In-depth interviews were conducted among 25 healthcare providers to explore data quality challenges in clinical notes. Quantitative data analysis used descriptive analysis via SPSS 27 software to determine data completeness and accuracy. In-depth interviews were conducted among 25 healthcare providers to explore data quality challenges. Qualitative data analysis utilized thematic analysis with hybrid inductive and deductive reasoning via NVivo 14 software. Clinical notes for prostate cancer management had poor quality. While the overall accuracy of documented variables was high at 1,494 (99.4%), documentation of key clinical variables was low; specifically, the clinical stage was documented in only 1,052 (70.0%) clinical notes, and the Gleason score in 923 (61.4%). Age, clinical presentation, and type of treatment showed high completeness. Themes established included lack of knowledge on data quality, poor integration of data, shortage of human resources for health, lack of supervision and the coexistence of electronic and manual record systems. The Health Information Management System and hospital-based cancer registries were not integrated. Documentation rates for clinical stage and Gleason score in the clinical notes of patients with prostate cancer were low in Tanzania. Improving the quality of clinical notes requires collaborative efforts among stakeholders focusing on structural reforms and capacity building of personnel.

PLOS Global Public HealthVol. 5(12)
Muhimbili University of Health and Allied Sciences (TZ), Mbeya University of Science and Technology (TZ), Kilimanjaro Christian Medical Centre (TZ), Muhimbili National Hospital (TZ), Bugando Medical Centre (TZ), Ocean Road Cancer Institute (TZ)
Openalex Percentile: Top 56%
Data Quality and Management
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.