Emotional experience of the diagnostic process of a rare disease and the perception of support systems: A scoping review

Abstract Background Rare diseases are difficult to diagnose. Due to their rarity, heterogeneity, and variability, rare diseases often result not only in extensive diagnostic tests and imaging studies, but also in unnecessary repetitions of examinations, which places a greater overall burden on the healthcare system. Diagnostic decision support systems (DDSS) optimized by rare disease experts and used early by primary care physicians and specialists are able to significantly shorten diagnostic processes. The objective of this study was to evaluate reductions in diagnostic costs incurred in rare disease cases brought about by rapid referral to an expert and diagnostic decision support systems. Methods Retrospectively, diagnostic costs from disease onset to diagnosis were analyzed in 78 patient cases from the outpatient clinic for rare inflammatory systemic diseases at Hannover Medical School. From the onset of the first symptoms, all diagnostic measures related to the disease were taken from the patient files and documented for each day. The basis for the health economic calculations was the Einheitlicher Bewertungsmaßstab (EBM) used in Germany for statutory health insurance, which assigns a fixed flat rate to the various medical services. For 76 cases we also calculated the cost savings that would have been achieved by the diagnosis support system Ada DX applied by an expert. Results The expert was able to achieve significant savings for patients with long courses of disease. On average, the expert needed only 27 % of the total costs incurred in the individual treatment odysseys to make the correct diagnosis. The expert also needed significantly less time and avoided unnecessary examination repetitions. If a DDSS had been applied early in the 76 cases studied, only 51–68 % of the total costs would have incurred and the diagnosis would have been made earlier. Earlier diagnosis would have significantly reduced costs. Conclusion The study showed that significant savings in the diagnostic process of rare diseases can be achieved through rapid referral to an expert and the use of DDSS. Faster diagnosis not only achieves savings, but also enables the right therapy and thus an increase in the quality of life for patients.

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Computerized Decision Support Systems for Nursing Homes: A Scoping Review

Journal of the American Medical Directors Association ◽

10.1016/j.jamda.2021.01.080 ◽

2021 ◽

Cited By ~ 1

Author(s):

Abir Abdellatif ◽

Jacques Bouaud ◽

Carmelo Lafuente-Lafuente ◽

Joël Belmin ◽

Brigitte Séroussi

Keyword(s):

Decision Support ◽

Nursing Homes ◽

Decision Support Systems ◽

Scoping Review ◽

Support Systems ◽

Computerized Decision Support ◽

Computerized Decision Support Systems

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Effects of Computerized Decision Support Systems on Management of Atrial Fibrillation: A Scoping Review

Journal of Atrial Fibrillation ◽

10.4022/jafib.1579 ◽

2017 ◽

Vol 10 (1) ◽

Cited By ~ 2

Author(s):

Reza Sheibani ◽

Ehsan Nabovati ◽

Mehdi Sheibani ◽

Ameen Abu-Hanna ◽

Alireza Heidari-Bakavoli ◽

...

Keyword(s):

Atrial Fibrillation ◽

Decision Support ◽

Decision Support Systems ◽

Scoping Review ◽

Support Systems ◽

Computerized Decision Support ◽

Computerized Decision Support Systems

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Dual Guidance Structure for Evaluation of Patients with Unclear Diagnosis in Centers for Rare Diseases (ZSE-DUO): Study Protocol for a Controlled Multi-center Cohort Study

10.21203/rs.3.rs-936612/v1 ◽

2021 ◽

Author(s):

Helge Hebestreit ◽

Cornalia Zeigler ◽

Christopher Schippers ◽

Martina de Zwaan ◽

Jürgen Deckert ◽

...

Keyword(s):

Mental Health ◽

Mental Disorders ◽

Rare Disease ◽

Rare Diseases ◽

Health Care Professionals ◽

Standard Care ◽

Controlled Study ◽

Diagnostic Process ◽

Two Phase ◽

Time To Diagnosis

Abstract Background In individuals suffering from a rare disease the diagnostic process and the confirmation of a final diagnosis often extends over many years. Factors contributing to delayed diagnosis include health care professionals' limited knowledge of rare diseases and frequent (co-)occurrence of mental disorders that may complicate and delay the diagnostic process. The ZSE-DUO study aims to assess the benefits of a combination of a physician focusing on somatic aspects with a mental health expert working side by side as a tandem in the diagnostic process.Study design This multi-center, prospective controlled study has a two-phase cohort design. Methods Two cohorts of 682 patients each are sequentially recruited from 11 university-based German Centers for Rare Diseases (CRD): the standard care cohort (control, somatic expertise only) and the innovative care cohort (experimental, combined somatic and mental health expertise). Individuals aged 12 years and older presenting with symptoms and signs which are not explained by current diagnoses will be included. Data will be collected prior to (T0) and at the first visit (T1) to the CRD’s outpatient clinic and 12 months thereafter (T2). Outcomes Primary outcome is the percentage of patients with one or more confirmed diagnoses covering the symptomatic spectrum presented. Sample size is calculated to detect a 10 percent increase from 30% in standard care to 40% in the innovative dual expert cohort. Secondary outcomes are a) time to diagnosis/diagnoses explaining the symptomatology; b) proportion of patients successfully referred from CRD to standard care; c) costs of diagnosis including incremental cost effectiveness ratios; d) predictive value of screening instruments administered at T0 to identify patients with mental disorders ; e) patients’ quality of life and evaluation of care; and f) physicians’ satisfaction with the innovative care approach. Conclusions This is the first multi-center study to investigate the effects of a mental health specialist working in tandem with a somatic expert physician in CRDs. If this innovative approach proves successful, it will be made available on a larger scale nationally and promoted internationally. In the best case, ZSE-DUO can significantly shorten the time to diagnosis for a suspected rare disease.

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E-Health Decision Support Systems for the Diagnosis of Dementia Diseases

E-Health Technologies and Improving Patient Safety: Exploring Organizational Factors ◽

10.4018/978-1-4666-2657-7.ch006 ◽

2013 ◽

pp. 84-97

Author(s):

Isabella Castiglioni ◽

Maria Carla Gilardi ◽

Francesca Gallivanone

Keyword(s):

Decision Support ◽

Decision Support Systems ◽

Support Systems ◽

Diagnostic Tools ◽

Diagnostic Process ◽

Dementia Patients ◽

Health Decision ◽

Diagnosis Of Dementia ◽

Dementia Diseases

The increase of incidence and prevalence of dementia diseases makes urgent the clinical community to be supported in the difficult diagnostic process of dementia patients. E-health decision support systems, based on innovative algorithms able to extract information from in vivo neuroimaging studies, can make a quite different way to perform neurological diagnosis and enlarge domains and actors involved in the diagnostic process. A number of image-processing methods that extract potential biomarkers from the in vivo neuroimaging studies have been proposed (e.g. volume segmentation, voxel-based statistical mapping). A number of new shape descriptors have also been developed (e.g. texture-based). Other approaches (e.g. machine learning, pattern recognition) have been proven effective, for both structural and functional data, in making automatic diagnoses. The integration of these sophisticated diagnostic tools into secure, efficient, and wide e-infrastructures is the prerequisite for the real implementation of e-health support services to the clinical and industrial communities managing dementia patients.

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Diagnosis of Rare Diseases: a scoping review of clinical decision support systems

Orphanet Journal of Rare Diseases ◽

10.1186/s13023-020-01536-z ◽

2020 ◽

Vol 15 (1) ◽

Author(s):

Jannik Schaaf ◽

Martin Sedlmayr ◽

Johanna Schaefer ◽

Holger Storf

Keyword(s):

Decision Support ◽

Data Integration ◽

Decision Support Systems ◽

Clinical Decision Support ◽

Clinical Data ◽

Rare Diseases ◽

Scoping Review ◽

Support Systems ◽

Clinical Decision Support Systems ◽

Clinical Decision

Abstract Background Rare Diseases (RDs), which are defined as diseases affecting no more than 5 out of 10,000 people, are often severe, chronic and life-threatening. A main problem is the delay in diagnosing RDs. Clinical decision support systems (CDSSs) for RDs are software systems to support clinicians in the diagnosis of patients with RDs. Due to their clinical importance, we conducted a scoping review to determine which CDSSs are available to support the diagnosis of RDs patients, whether the CDSSs are available to be used by clinicians and which functionalities and data are used to provide decision support. Methods We searched PubMed for CDSSs in RDs published between December 16, 2008 and December 16, 2018. Only English articles, original peer reviewed journals and conference papers describing a clinical prototype or a routine use of CDSSs were included. For data charting, we used the data items “Objective and background of the publication/project”, “System or project name”, “Functionality”, “Type of clinical data”, “Rare Diseases covered”, “Development status”, “System availability”, “Data entry and integration”, “Last software update” and “Clinical usage”. Results The search identified 636 articles. After title and abstracting screening, as well as assessing the eligibility criteria for full-text screening, 22 articles describing 19 different CDSSs were identified. Three types of CDSSs were classified: “Analysis or comparison of genetic and phenotypic data,” “machine learning” and “information retrieval”. Twelve of nineteen CDSSs use phenotypic and genetic data, followed by clinical data, literature databases and patient questionnaires. Fourteen of nineteen CDSSs are fully developed systems and therefore publicly available. Data can be entered or uploaded manually in six CDSSs, whereas for four CDSSs no information for data integration was available. Only seven CDSSs allow further ways of data integration. thirteen CDSS do not provide information about clinical usage. Conclusions Different CDSS for various purposes are available, yet clinicians have to determine which is best for their patient. To allow a more precise usage, future research has to focus on CDSSs RDs data integration, clinical usage and updating clinical knowledge. It remains interesting which of the CDSSs will be used and maintained in the future.

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A scoping review and proposed workflow for multi-omic rare disease research

Orphanet Journal of Rare Diseases ◽

10.1186/s13023-020-01376-x ◽

2020 ◽

Vol 15 (1) ◽

Cited By ~ 3

Author(s):

Katie Kerr ◽

Helen McAneney ◽

Laura J. Smyth ◽

Caitlin Bailie ◽

Shane McKee ◽

...

Keyword(s):

Rare Disease ◽

Scoping Review ◽

Disease Research

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The use of machine learning in rare diseases: a scoping review

Orphanet Journal of Rare Diseases ◽

10.1186/s13023-020-01424-6 ◽

2020 ◽

Vol 15 (1) ◽

Cited By ~ 1

Author(s):

Julia Schaefer ◽

Moritz Lehne ◽

Josef Schepers ◽

Fabian Prasser ◽

Sylvia Thun

Keyword(s):

Machine Learning ◽

Rare Disease ◽

Rare Diseases ◽

Scoping Review ◽

Input Data ◽

Skin Diseases ◽

Demographic Data ◽

Support Vector ◽

Research Activity ◽

Data Set

Abstract Background Emerging machine learning technologies are beginning to transform medicine and healthcare and could also improve the diagnosis and treatment of rare diseases. Currently, there are no systematic reviews that investigate, from a general perspective, how machine learning is used in a rare disease context. This scoping review aims to address this gap and explores the use of machine learning in rare diseases, investigating, for example, in which rare diseases machine learning is applied, which types of algorithms and input data are used or which medical applications (e.g., diagnosis, prognosis or treatment) are studied. Methods Using a complex search string including generic search terms and 381 individual disease names, studies from the past 10 years (2010–2019) that applied machine learning in a rare disease context were identified on PubMed. To systematically map the research activity, eligible studies were categorized along different dimensions (e.g., rare disease group, type of algorithm, input data), and the number of studies within these categories was analyzed. Results Two hundred eleven studies from 32 countries investigating 74 different rare diseases were identified. Diseases with a higher prevalence appeared more often in the studies than diseases with a lower prevalence. Moreover, some rare disease groups were investigated more frequently than to be expected (e.g., rare neurologic diseases and rare systemic or rheumatologic diseases), others less frequently (e.g., rare inborn errors of metabolism and rare skin diseases). Ensemble methods (36.0%), support vector machines (32.2%) and artificial neural networks (31.8%) were the algorithms most commonly applied in the studies. Only a small proportion of studies evaluated their algorithms on an external data set (11.8%) or against a human expert (2.4%). As input data, images (32.2%), demographic data (27.0%) and “omics” data (26.5%) were used most frequently. Most studies used machine learning for diagnosis (40.8%) or prognosis (38.4%) whereas studies aiming to improve treatment were relatively scarce (4.7%). Patient numbers in the studies were small, typically ranging from 20 to 99 (35.5%). Conclusion Our review provides an overview of the use of machine learning in rare diseases. Mapping the current research activity, it can guide future work and help to facilitate the successful application of machine learning in rare diseases.

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