Turnaround Time and Barriers in Treatment of Newly Diagnosed Cancer Patients: A Research Protocol
Published: May 1, 2024 | DOI: https://doi.org/10.7860/JCDR/2024/68124.19393
Harshali Himmat Pal, Jitesh Pankwase
1. Master of Hospital Administration, Department of Hospital Administration, Datta Meghe Institute of Higher Education and Reasearch, Wardha, Maharashtra, India.
2. Administrative Officer, Department of Hospital Administration, Datta Meghe Institute of Higher Education and Reasearch, Wardha, Maharashtra, India.
Correspondence
Harshali Himmat Pal,
Sukhkarta Nagari, Sawangi Meghe, Wardha-442001, Maharashtra, India.
E-mail: h7584500@gmail.com
Introduction: The journey from cancer diagnosis to treatment initiation is a critical period in a patient’s life. Timely treatment is often associated with better outcomes, yet numerous challenges and barriers can impede the process.
Need of the Study: This research will hold significant implications for healthcare providers, policymakers, and, most importantly, cancer patients. By uncovering the obstacles and delays in cancer treatment, this protocol will provide the information needed to streamline the process, potentially leading to earlier interventions, improved patient experiences, and enhanced treatment outcomes.
Aim: To comprehensively understand and address the factors influencing the turnaround time and the barriers encountered by cancer patients in accessing treatment.
Materials and Methods: This study will employ a mixed-methods research design, combining both quantitative and qualitative approaches. It will encompass 89 patients at Siddharth Gupta Memorial Cancer Hospital (SGMCH), situated in Sawangi (Meghe), Wardha, Maharashtra, India. The estimated duration for this study is from December 2023 to October 2024. Factors influencing the turnaround time and the barriers encountered by cancer patients in accessing treatment will be evaluated. Primary data will be collected directly from newly diagnosed cancer patients using structured surveys/questionnaires. Secondary data sources include medical records, hospital, and healthcare system data.
Statistical analysis will be done using Chi-square for qualitative measurement, Independent t-test, and Analysis of Variance (ANOVA) for the quantitative measurement. A p-value of <0.05 will be considered significant.
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