NEWPORT INTERNATIONAL JOURNAL OF SCIENTIFIC AND EXPERIMENTAL SCIENCES (NIJSES)

Volume 3 Issue 3 2023

The electronic Patient Diagnosis Management System in Naguru Teenage Information and Health Center

Okidi Jacob

Faculty of Computer Science and Information Technology Kampala International University Uganda.

ABSTRACT

Electronic diagnosis management system is intended to help medics and clinicians in diagnosing clinical cases. Most of the clinicians are still relying on manual clinical diagnosis process. A manual clinical diagnosis is a very complex, cumbersome and error prone process; even very experienced doctors sometimes fail to diagnose a clinical condition correctly at an early stage. Clinical decision support systems assist the clinician in applying new information to patient care through the analysis of patient-specific clinical variables. Many of these systems are used to enhance diagnostic efforts and include computer-based programs such as Dxplain that provide extensive differential diagnoses based on clinical information entered by the clinician. Other forms of clinical decision support systems, including antibiotic management programs and anticoagulation dosing calculators, seek to prevent medical errors and improve patient safety. The electronic patient diagnosis management system is basically to help diagnose medical health problems (sickness) of patients by enabling them input the symptoms they are experiencing, the system then analyses them and gives feedback to what the patient could be suffering from. It as well helps to find more information on the symptoms or signs of the disease a patient is suffering from.

Keywords: Patients, Clinicians, Electronic diagnosis management, Clinical decision support systems, Diagnostic efforts.

INTRODUCTION

Clinical diagnosis is diagnosis or the process of accessing the nature of the disease based on its signs, symptoms and laboratory findings. In computer assisted diagnosis, a computer program identifies the disease(s) that fit the identified abnormalities best. Clinical diagnosis can also be defined as an act/process of identifying the nature and cause of a disease or a medical element through carefully studying patient’s symptoms and signs, evaluation of patient history, clinical and laboratory examinations [1]. A clinical diagnosis is the most important and critical part of medical and health care system and is vital for treating patients. Many research organizations and companies researched have developed clinical diagnosis similar support systems using various software and computer technologies. Clinical diagnosis support software systems help clinicians in diagnosing clinical cases. Most clinicians are still relying on manual clinical diagnosis processes [2, 3]. A manual clinical diagnosis is a very complex, cumbersome and error-prone process; even very experienced doctors sometimes fail to diagnose a clinical condition correctly at an early stage. This project was purposely intended to design and implement a reliable electronic patient diagnosis management system to be used at Naguru Teenage Information/Health Center. Since ancient times, human beings have been trying to solve human problems with inventions and technology. Such inventions and technologies are no exception to solving human illness. Scientists and doctors have always tried to use the latest technological inventions in healthcare for example Rontgen did not invent X-rays to diagnose human elements but it was successfully implemented in healthcare and the technology saved millions of human lives; similar story with other technologies like Microscope, CT Scan, MRI, pacemaker, artificial hearts, prosthetics, robotic surgical arms, digital ECG, EEG and so on, last but not least computers and information technology also includes in that technological adaptations [4]. In the early days of medical-related computer technology, the notion of that computer technology could help solve healthcare problems developed much interest and enthusiasm and was prompted for pursuing the use of computers in the medical field. For the past four decades, a new branch of information technology is emerged and proliferated called Health Informatics or Medical Informatics. The case study of this project was Naguru teenage information/health centre (NTIHC). It is located in Bugolobi, approximately 6km from the heart of Kampala city. NTIHC is a pioneer program in providing adolescent sexual reproductive health services in Uganda. It was started as a voluntary activity in 1994 by a group of professional volunteers. Using the recommended method of manually entering key findings, the list of diagnoses suggested by Isabel contained the correct diagnosis in 48 of the 50 cases (96%). Typically, 3–6 key findings from each case were used. The 2 diagnoses that were not suggested (progressive multifocal encephalopathy and nephrogenic fibrosing dermopathy) were not included in the Isabel database at the time of the study; thus, these 2 cases would never have been suggested, even with different keywords. Using the copy/paste method for entering the whole text, the list of diagnoses suggested by Isabel contained the correct diagnosis in 37 of the 50 cases (76%). Isabel presented 10 diagnoses on the first web page and 10 additional diagnoses on subsequent pages up to a total of 30 diagnoses. Because users may tend to disregard suggestions not shown on later web pages, we tracked this parameter for the copy/paste method of data entry: The correct diagnosis was presented on the first page in 19 of the 37 cases (51%) or first two pages in 28 of the 37 cases (77%). Similar data were not collected for manual data entry because the order of presentation depended on which key findings were entered. Both data entry approaches were fast: Manually entering data and obtaining diagnostic suggestions typically required less than 1 minute per case, and the copy/paste method typically required less than 5 seconds. Diagnostic errors are an underappreciated cause of the medical error, and any intervention that has the potential to produce correct and timely medical diagnosis is worthy of serious consideration. Our recent analysis of diagnostic errors in Internal Medicine found that clinicians often stop thinking after arriving at a preliminary diagnosis that explains all the key findings, leading to context errors and ‘premature closure’, where further possibilities are not considered. These and other errors contribute to diagnoses that are wrong or delayed, causing substantial harm to the patients affected. Systems that help clinicians explore a more complete range of diagnostic possibilities could conceivably reduce these types of errors. Many different CDSSs have been developed over the years, and these typically matched the manually entered features of the case in question to a database of key findings abstracted from experts or the clinical literature. The sensitivity of these systems was in the range of 50%–60%, and the time needed to access and query the database was often several minutes. More recently, the possibility of using Google to search for clinical diagnoses has been suggested. However, a formal evaluation of this approach on a subset of the same “Case Records” cases used in our study found a sensitivity of 58%, in the range of the first-generation CDSSs and unacceptably low for clinical use. The findings of our study indicate that CDSS products have evolved substantially [5-7]. Using the Isabel CDSS, we found that data entry takes under 1 minute, and the sensitivity in a series of high histories using copy/paste functionality allowed even faster data entry but reduced sensitivity. The loss of sensitivity seemed primarily related to negative findings included in the pasted history and physical (e.g., “the patient denies chest pain”), which are treated as positive findings (chest pain) by the search algorithm [8]. There are several relevant limitations of this study that make it difficult to predict how Isabel might perform as a diagnostic aid in clinical practice. First, the results obtained here reflect the theoretical upper limit of performance, given that an investigator who was aware of the correct diagnosis entered the key findings. Further, clinicians in real life seldom have the wealth of reliable and organized information that is presented in the Case Records or the time needed to use a CDSS in every case [9]. To the extent that Isabel functions as a ‘learned intermediary’, success in using the program will also clearly depend on the clinical expertise of the user and their facility in working with Isabel. A serious existential concern is whether presenting a clinician with dozens of diagnostic suggestions might be a distraction or lead to unnecessary testing. We have previously identified these trade-offs as an unavoidable cost of improving patient safety: The price of improving the odds of reaching a correct diagnosis is the extra time and resources consumed in using the CDSS and considering alternative diagnoses that might turn out to be irrelevant. In summary, the Isabel CDSS performed quickly and accurately in suggesting correct diagnoses for complex adult medicine cases [2, 7]. However, the test setting was artificial, and the CDSS should be evaluated in more natural environments for its potential to support clinical diagnosis and reduce the rate of diagnostic error in medicine. Therefore, this chapter discussed the literature review of the proposed system. The views and opinions of other researchers regarding the concept. The performances of such systems are clearly shown as well as their challenges.

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CITE AS:Okidi Jacob (2023). The electronic Patient Diagnosis Management System in Naguru Teenage Information and Health Center. NEWPORT INTERNATIONAL JOURNAL OF SCIENTIFIC AND EXPERIMENTAL SCIENCES (NIJSES) 3(3)1- 21.

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