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Designing a decision support tool for early diagnosis and intervention in multiple sclerosis
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Cheriyan, Jojy. Designing a decision support tool for early diagnosis and intervention in multiple sclerosis. Retrieved from https://doi.org/doi:10.7282/t3-yjmw-wy25

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TitleDesigning a decision support tool for early diagnosis and intervention in multiple sclerosis
NameCheriyan, Jojy (author); Mital PhD, Dinesh (chair); Srinivasan PhD, Shankar (co-chair); Vyas PhD, Riddhi (internal member); Rutgers University; School of Health Professions
Date Created2020
Other Date2020-01 (degree)
SubjectMultiple sclerosis -- Diagnosis, Biomedical Informatics
Extent1 online resource (xv, 137 pages) : illustrations
DescriptionMultiple Sclerosis (MS) is one of the most common neurological disorders that leads to disability at a younger age among people in North America and Europe. It is a non-communicable disease with no cure, debilitated by physical and mental impairments. Recent reports show an increase in the incidence of MS in United States that is twice more than the past estimate. The average period to diagnose MS still ranges from 6 months to 3 years. Studies suggest that early diagnosis and intervention can delay the progression of the disease and improve the quality of life. Until today no clinical decision support exists that could be used to assist clinicians in diagnosing MS at early onset of symptoms. This study has been conducted to assess the need and explore the quantifiable predictors that could be used for helping clinicians in early detection of disease activity. A review of literature followed by a quasi-experimental approach has been done to collect predictors and analyze the trending incidence of MS in United States. A survey was also conducted to seek the expert opinions of neurologists and primary care physicians (PCP's) to analyze the existence and effectiveness of clinical decision support system in MS.

The literature review took into account various theories about the etiology of MS but found that some old theories are not relevant compared to the disease trends during the last two decades. The in-depth review found that some strong predictors do exist that could help clinicians in early diagnosis of the disease. The results obtained by analyzing HCUP data substantiate the rising incidence and prevalence of MS in United States and corroborates the higher incidence among young women getting diagnosed with MS. The survey analysis shows that currently no clinical decision support system (CDSS) exists to diagnose MS early at the point of care. On a scale of 1 to 5 with 1 being strongly disagreed and 5 being strongly agreed, the neurologists have a response average of 1.6 and PCP's of 2.6 about the effectiveness of current techniques in the early diagnosis of MS. Among both groups 95% of clinicians haven’t heard or used any CDSS at the point of care. Both groups agreed that a CDSS may help in early diagnosis, quality care and reducing unnecessary tests and costs with an average response rates of 3.9 and 4.0.

The predictors selected are weighted and used to design a clinical decision support system and tested on a sample population of MS patients who are diagnosed to have MS. On a weighted scale of 265 to 455 the average output is 341.6 with a 95% CI ranging between 325.94 and 357.26. Based on the outputs obtained from the expert system the study concludes that it is possible to use a clinical decision support system at the point of care to assist clinicians in diagnosing MS at an early stage. More testing needs to be done by a multi-centric research due to the variabilities and inconsistencies existing in the clinical manifestations as well as pathologic phenomena in MS.
NotePh.D.
NoteIncludes bibliographical references
Genretheses, ETD doctoral
Persistent URLhttps://doi.org/doi:10.7282/t3-yjmw-wy25
LanguageEnglish
CollectionSchool of Health Professions ETD Collection
Organization NameRutgers, The State University of New Jersey
RightsThe author owns the copyright to this work.
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