Implementation of intelligent process automation (IPA) based clinical decision support system for early detection and screening of diabetes : this thesis is presented in partial fulfilment of the requirements for the degree of Master of Information Sciences in Information Technology, School of Natural and Computational Sciences at Massey University Albany, Auckland, New Zealand
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2021
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Massey University
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Abstract
Diabetes mellitus has become a leading cause of disease-related deaths in the world. Once an individual is diagnosed with diabetes, a series of processes will be required to keep the blood sugar regular and help avoid hyperglycemia and
hypoglycemia. Self-Management of diabetes is complex and involves constant glucose monitoring, diet management, care, support, exercise, and insulin management. These processes are expensive because they require detailed record-keeping of medications, activities, and a timely report to doctors to assist them in making an informed decision that will subsequently help the patient heal. Other challenges include the high cost of treatment, lifestyle changes, education, lack of medication adherence, and treatment plans.
Our approach is to adopt the Early screening technique and detect the risk of diabetes unobtrusively. Early screening is a technique that can help detect Type 1, 2 diabetes and achieve preventive care according to the guidelines set by WHO and recommended by the American Diabetes Association (ADA). Unobtrusive systems allow a doctor to screen for diabetes while he is unaware.
We followed the Design Science Research model (DSRM) and started by using systematic literature review (SLR) guidelines to search the most popular journals limiting the results tied to studies that discussed the screening and detection of the risk of diabetes.
We reviewed the architecture, features, and limitations of the various tools and technologies using the following classification: Continuous Glucose Monitoring Systems (CGMS), Flash Glucose Monitoring Systems (FGMS), and
the Unobtrusive Systems. In addition, under the unobtrusive system, we studied the Child Health Improvement through Computer Automation (CHICA) system. While there is evidence that supports its benefits and usefulness, we
found some required enhancements from the literature in the areas of decision support systems, data entry automation, and flexible integration with other systems.
The artefact built during the development phase is an Intelligent process automation (IPA) system that can be implemented within the health sector for early screening and detection of diabetes unobtrusively. Developing this artefact will allow us to understand the possible issues and challenges of implementing an automation process in a
medical institution. We evaluated the artefact using a mix of quantitative and qualitative methods. This method allowed us to answer the research questions and understand the value of automation to medical practitioners. The value includes speed, reduce cost, and error while safeguarding the lives of the medical professional on active duty.
The results show that the system can enhance patient-doctor interaction, reduce patient wait time, and optimize the glucose monitoring process. However, there were challenges such as cost of implementation, training of staff, and the
increased workload within the system. In addition, potential challenges identified include fear of job loss and aversion to change during implementation within the hospital.
This study has also allowed us to understand the integration of robotic process automation with machine learning within the healthcare sector. We hope that this study will contextually position IPA within the technological stack of health care institutions and add to the body of knowledge on this subject.