[Computer-assisted diagnosis of rare diseases]

The economic burden of diagnostic uncertainty on rare disease patients

BACKGROUND

It often takes a long time before a rare disease is diagnosed. Without a diagnosis, the right therapy often cannot be carried out and without the right therapy, the patients are denied the opportunity for a cure or relief from their symptoms. In addition, rare diseases can also have economic consequences for those affected. This study aimed to investigate the extent to which a rare disease affects the income and work performance of the patients concerned and whether the use of AI in diagnostics would have the potential to reduce economic losses.

METHODS

The work performance and income of 71 patients of the outpatient clinic for rare inflammatory systemic diseases with renal involvement at Hannover Medical School were analyzed during the course of the disease. The WHO Health and Work Performance Questionnaire (HPQ) was used to collect data. During the patient interviews, the questionnaire was completed four times: at the onset of the first symptoms, when a diagnostic decision support system (DDSS) would have suggested the correct diagnosis, at the time of diagnosis and at the current status.

RESULTS

With the onset of the diagnostic odyssey, the monthly net income of the patients under study dropped by an average of 5.32% due to lower work performance or work absenteeism. With the correct diagnosis, the original or even a better income of 11.92% could be achieved. Loss of income due to illness was more massive in patients with a rare disease with joint, muscle and connective tissue involvement than in patients with rare vasculitides. If a DDSS had been used, the loss of income would have been 2.66% instead of the actual 5.32%.

CONCLUSION

Rare diseases resulted in temporary or existing income losses in 28.17% of the patients. Losses in work performance and income were related to the type of disease and were more pronounced in patients with joint, muscle or connective tissue disease than in patients with rare vasculitides. The use of a DDSS may have the potential to reduce the negative income effects of patients through earlier correct diagnosis.

TRIAL REGISTRATION

Retrospectively registered.

Requirements analysis for an AI-based clinical decision support system for general practitioners: a user-centered design process

BACKGROUND

As the first point of contact for patients with health issues, general practitioners (GPs) are frequently confronted with patients presenting with non-specific symptoms of unclear origin. This can result in delayed, prolonged or false diagnoses. To accelerate and improve the diagnosis of diseases, clinical decision support systems would appear to be an appropriate tool. The objective of the project 'Smart physician portal for patients with unclear disease' (SATURN) is to employ a user-centered design process based on the requirements analysis presented in this paper to develop an artificial Intelligence (AI)-based diagnosis support system that specifically addresses the needs of German GPs.

METHODS

Requirements analysis for a GP-specific diagnosis support system was conducted in an iterative process with five GPs. First, interviews were conducted to analyze current workflows and the use of digital applications in cases of diagnostic uncertainty (as-is situation). Second, we focused on collecting and prioritizing tasks to be performed by an ideal smart physician portal (to-be situation) in a workshop. We then developed a task model with corresponding user requirements.

RESULTS

Numerous GP-specific user requirements were identified concerning the tasks and subtasks: performing data entry (open system, enter patient data), reviewing results (receiving and evaluating results), discussing results (with patients and colleagues), scheduling further diagnostic procedures, referring to specialists (select, contact, make appointments), and case closure. Suggested features particularly concerned the process of screening and assessing results: e.g., the system should focus more on atypical patterns of common diseases than on rare diseases only, display probabilities of differential diagnoses, ensure sources and results are transparent, and mark diagnoses that have already been ruled out. Moreover, establishing a means of using the platform to communicate with colleagues and transferring patient data directly from electronic patient records to the system was strongly recommended.

CONCLUSIONS

Essential user requirements to be considered in the development and design of a diagnosis system for primary care could be derived from the analysis. They form the basis for mockup-development and system engineering.

Rare disease curative care expenditure-financing scheme-health provider-beneficiary group analysis: an empirical study in Sichuan Province, China

Background

Rare diseases impose a heavy economic burden on patients’ families and society worldwide. This study used the samples from Sichuan Province in China to estimate the curative care expenditure (CCE) of ten rare diseases, for supporting the prioritization of rare disease health policies.

Methods

Multi-stage cluster sampling method was adopted to investigate 9714 rare disease patients from 1556 medical institutions in Sichuan Province. Based on the System of Health Accounts 2011, this study estimated the total CCE of 10 rare diseases, financing schemes, and their allocation among different medical institutions and groups of people.

Results

In 2018, the total CCE of the ten rare diseases was $19.00 million, the three costliest rare diseases were Hemophilia ($4.38 million), Young-onset Parkinson’s disease ($2.96 million), and Systemic Sclerosis ($2.45 million). Household out-of-pocket expenditure (86.00% for outpatients, 41.60% for inpatients) and social health insurance (7.85% for outpatients; 39.58% for inpatients) were the main sources of financing CCE. The out-of-pocket expenditures for patients with Young-onset Parkinson’s disease, Congenital Scoliosis, and Autoimmune Encephalitis accounted for more than 60% of the total CCE. More than 80% of the rare disease CCE was incurred in general hospitals. The 40–59 age group accounted for the highest CCE (38.70%) while men spent slightly more (55.37%) than women (44.64%).

Conclusions

As rare disease treatment is costly and household out-of-pocket expenditure is high, we suggest taking steps to include rare disease drugs in the National Reimbursement Drug List and scientifically re-design insurance coverage. It is also necessary to explore a multi-tiered healthcare security system to pay for the CCE of rare diseases and reduce the economic burden on patients.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13023-022-02524-1.

A Novel Diagnostic Decision Support System for Medical Professionals: Prospective Feasibility Study

BACKGROUND

Continuously growing medical knowledge and the increasing amount of data make it difficult for medical professionals to keep track of all new information and to place it in the context of existing information. A variety of digital technologies and artificial intelligence-based methods are currently available as persuasive tools to empower physicians in clinical decision-making and improve health care quality. A novel diagnostic decision support system (DDSS) prototype developed by Ada Health GmbH with a focus on traceability, transparency, and usability will be examined more closely in this study.

OBJECTIVE

The aim of this study is to test the feasibility and functionality of a novel DDSS prototype, exploring its potential and performance in identifying the underlying cause of acute dyspnea in patients at the University Hospital Basel.

METHODS

A prospective, observational feasibility study was conducted at the emergency department (ED) and internal medicine ward of the University Hospital Basel, Switzerland. A convenience sample of 20 adult patients admitted to the ED with dyspnea as the chief complaint and a high probability of inpatient admission was selected. A study physician followed the patients admitted to the ED throughout the hospitalization without interfering with the routine clinical work. Routinely collected health-related personal data from these patients were entered into the DDSS prototype. The DDSS prototype's resulting disease probability list was compared with the gold-standard main diagnosis provided by the treating physician.

RESULTS

The DDSS presented information with high clarity and had a user-friendly, novel, and transparent interface. The DDSS prototype was not perfectly suited for the ED as case entry was time-consuming (1.5-2 hours per case). It provided accurate decision support in the clinical inpatient setting (average of cases in which the correct diagnosis was the first diagnosis listed: 6/20, 30%, SD 2.10%; average of cases in which the correct diagnosis was listed as one of the top 3: 11/20, 55%, SD 2.39%; average of cases in which the correct diagnosis was listed as one of the top 5: 14/20, 70%, SD 2.26%) in patients with dyspnea as the main presenting complaint.

CONCLUSIONS

The study of the feasibility and functionality of the tool was successful, with some limitations. Used in the right place, the DDSS has the potential to support physicians in their decision-making process by showing new pathways and unintentionally ignored diagnoses. The DDSS prototype had some limitations regarding the process of data input, diagnostic accuracy, and completeness of the integrated medical knowledge. The results of this study provide a basis for the tool's further development. In addition, future studies should be conducted with the aim to overcome the current limitations of the tool and study design.

TRIAL REGISTRATION

ClinicalTrials.gov NCT04827342; https://clinicaltrials.gov/ct2/show/NCT04827342.

[What is confirmed in the diagnostics of autoinflammatory fever diseases?]

Periodic fever syndromes (PFS) are a group of rare autoinflammatory diseases, which are characterized by disorders of the innate immune reaction and life-long recurrent episodes of inflammatory symptoms. This article describes the diagnostic approach. In addition to the patient medical history, physical examination and laboratory determinations, gene tests are becoming increasingly more important. The panel diagnostics using high throughput sequencing or next generation sequencing (NGS) is the method of choice for the detection of a genetic cause of PFS. This article discusses the diagnostic decision support systems (DDSS) that can play a future role in the diagnosis of rare diseases, especially those with complex patterns of symptoms.

[Digital diagnostic support in rheumatology]

Only the correct diagnosis enables an effective treatment of rheumatic diseases. Digitalization has already significantly accelerated and simplified our everyday life. An increasing number of digital options are available to patients and medical personnel in rheumatology to accelerate and improve the diagnosis. This work gives an overview of current developments and tools for patients and rheumatologists, regarding digital diagnostic support in rheumatology.

Health economic benefits through the use of diagnostic support systems and expert knowledge

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.

[European Reference Network for Rare Hepatological Diseases (ERN RARE-LIVER)]

Patients with rare diseases often receive insufficient medical care. The European Reference Networks (ERNs) were initiated by the European Union to improve healthcare for patients with rare and complex diseases within Europe. The Reference Network on Hepatological Diseases (ERN RARE-LIVER), which consists of hepatological centres, scientific societies and numerous patient organizations, is one of 24 ERNs. The aim of ERN RARE-LIVER is high-quality healthcare for patients suffering from rare liver diseases, regardless of their place of residence. Standardization of treatment, coordination of research projects as well as training and teaching of patients, patient representatives and healthcare professionals are means to reach this goal. Virtual case discussions are offered via a web-based platform (Clinical Patient Management System), in which experts from the ERNs advise treating physicians on the diagnosis and therapy of rare diseases.

[What entertainment television can do to convey medical knowledge to students and laypeople-raising awareness of rare diseases]

Menschen mit komplexen und seltenen Erkrankungen haben es in unserem Gesundheitssystem oft schwer. Bis zur Diagnosefindung kann es Jahre dauern und häufig fehlt eine geeignete Therapie. Dabei sind seltene Erkrankungen in der Summe der Patienten alles andere als selten: Allein in Deutschland sind etwa 4 Mio. Menschen betroffen. Dennoch gilt, dass eine seltene Erkrankung oft erst dann entdeckt werden kann, wenn sie bekannt genug ist und die Bevölkerung für ihre Existenz sensibilisiert ist – dies gilt sowohl für Laien als auch die Ärzteschaft. Die eher ungewöhnliche Form der Wissensvermittlung über das Unterhaltungsfernsehen kann einen wichtigen Beitrag zur Verbreitung von medizinischem Wissen und zur Sensibilisierung für medizinische Themen leisten. In konkreten Fällen kann das Unterhaltungsfernsehen so zur Diagnosefindung bei seltenen Erkrankungen beitragen oder Laien zu lebensrettenden Maßnahmen ermutigen, was in diesem Artikel anhand einiger Fallbeispiele verdeutlicht wird.

Serien und Quizshows erreichen sehr viel mehr Zuschauer als klassische Gesundheitssendungen. Auch im Studierendenunterricht haben sie sich als außergewöhnlich wirksam erwiesen. Da die Erzählform das Mitfiebern und Mitraten in den Mittelpunkt stellt; anstelle des reinen Vermittelns von Fakten werden die medizinischen Themen als Gedächtnisinhalte emotional stärker verankert und leichter erinnerlich. Das Unterhaltungsfernsehen bietet somit einen innovativen Ansatz, um die Gesundheitskompetenz der Bevölkerung zu steigern – ein Potenzial, das in Deutschland noch besser genutzt werden könnte.

[Evaluation of a future scenario concerning the use of big data applications to improve the care of people with rare diseases]

INTRODUCTION

In Germany there are about 4 million people living with a rare disease. Studies have shown that big data applications can improve diagnosis of and research on rare diseases more effectively. However, no concrete comprehensive concept for the use of big data in the care of people with rare diseases has so far been established in Germany. As part of the project "BIDA-SE", which is funded by the German Ministry of Health, a first scenario has been designed to show how big data applications can be usefully incorporated into the care of people with rare diseases.

METHODS

The aim of the present study was to evaluate this scenario with regard to acceptance, (clinical) benefits, economic aspects, and limitations and barriers to its implementation. To evaluate the scenario, an online survey was conducted in Germany in October/November 2019 amongst a total of N = 9 physicians, N = 69 patients with rare diseases/patient representatives, N = 14 IT experts and N = 21 health care researchers. The online questionnaire consisted of both standardized, validated questions taken from already tested survey instruments and additional questions which were constructed on the basis of a preceding literature analysis. The evaluation of the survey was primarily descriptive, with a calculation of frequencies, mean values and standard deviations.

RESULTS

The results of the evaluation show that the scenario has been accepted by a majority of all groups surveyed (physicians, patients/patient representatives, IT experts and health care researchers). From the point of view of physicians, patients/patient representatives and health care researchers, the scenario has the potential to accelerate the diagnosis and initiation of therapy and to improve cross-sectoral treatment. From the physician's and health care researcher's perspective, investments in the application presented in the scenario would be profitable. Financing the scenario would, however, require adjusting the reimbursement situation. The limitations and barriers identified by all groups for a medium-term implementation of the scenario can be grouped into seven thematic areas where action is needed: (1) financing and investment, (2) data protection and data security, (3) standards/data sources/data quality, (4) acceptance of technology, (5) integration into the daily work routine, (6) knowledge about availability as well as (7) habits and preferences/physician's role.

DISCUSSION

With the present study, a first interdisciplinary, practical scenario using big data applications was evaluated with regard to acceptance, benefits and limitations/barriers. The scenario is widely accepted among the majority of all surveyed target groups and is considered (clinically) useful, although legal, organisational and technical barriers still need to be overcome for its medium-term implementation. The evaluation results contribute to the derivation of recommendations for action to ensure the medium-term implementation of the scenario and to channel access to the Centres for Rare Diseases in the future.

CONCLUSION

Many activities have been initiated at a national level to improve the health care situation of people with rare diseases. The scenario developed in the "BIDA-SE" project complements these research activities and illustrates how big data applications can be usefully implemented into practice to improve the diagnosis and therapy of people with rare diseases in a sustainable way.

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

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.

Interviews with experts in rare diseases for the development of clinical decision support system software - a qualitative study

BACKGROUND

Patients with rare diseases (RDs) are often diagnosed too late or not at all. Clinical decision support systems (CDSSs) could support the diagnosis in RDs. The MIRACUM (Medical Informatics in Research and Medicine) consortium, which is one of four funded consortia in the German Medical Informatics Initiative, will develop a CDSS for RDs based on distributed clinical data from ten university hospitals. This qualitative study aims to investigate (1) the relevant organizational conditions for the operation of a CDSS for RDs when diagnose patients (e.g. the diagnosis workflow), (2) which data is necessary for decision support, and (3) the appropriate user group for such a CDSS.

METHODS

Interviews were carried out with RDs experts. Participants were recruited from staff physicians at the Rare Disease Centers (RDCs) at the MIRACUM locations, which offer diagnosis and treatment of RDs. An interview guide was developed with a category-guided deductive approach. The interviews were recorded on an audio device and then transcribed into written form. We continued data collection until all interviews were completed. Afterwards, data analysis was performed using Mayring's qualitative content analysis approach.

RESULTS

A total of seven experts were included in the study. The results show that medical center guides and physicians from RDC B-centers (with a focus on different RDs) are involved in the diagnostic process. Furthermore, interdisciplinary case discussions between physicians are conducted. The experts explained that RDs exist which cannot be fully differentiated, but rather described only by their overall symptoms or findings: diagnosis is dependent on the disease or disease group. At the end of the diagnostic process, most centers prepare a summary of the patient case. Furthermore, the experts considered both physicians and experts from the B-centers to be potential users of a CDSS. The experts also have different experiences with CDSS for RDs.

CONCLUSIONS

This qualitative study is a first step towards establishing the requirements for the development of a CDSS for RDs. Further research is necessary to create solutions by also including the experts on RDs.

[Information technology and eHealth to improve patient safety]

Patient safety is a key element of high-quality healthcare. Digitalization, new eHealth applications and data-based algorithms have high potential to make a significant contribution. This article presents current technological developments along a simplified patient journey from emergency medical triage, diagnosis and therapy to follow-up. The technical interventions are highly diverse and mostly accompanied by a low level of evidence, since most of them are from single academic projects or start-ups. Although there should be no doubt that technology is an important instrument for increasing patient safety, new technologies also involve new risks. Furthermore, technical measures must always be embedded in an overall concept of organizational measures, adequate education, training and accompanying research in order to generate the highest possible benefits and lowest possible risks.

Can a decision support system accelerate rare disease diagnosis? Evaluating the potential impact of Ada DX in a retrospective study

BACKGROUND

Rare disease diagnosis is often delayed by years. A primary factor for this delay is a lack of knowledge and awareness regarding rare diseases. Probabilistic diagnostic decision support systems (DDSSs) have the potential to accelerate rare disease diagnosis by suggesting differential diagnoses for physicians based on case input and incorporated medical knowledge. We examine the DDSS prototype Ada DX and assess its potential to provide accurate rare disease suggestions early in the course of rare disease cases.

RESULTS

Ada DX suggested the correct disease earlier than the time of clinical diagnosis among the top five fit disease suggestions in 53.8% of cases (50 of 93), and as the top fit disease suggestion in 37.6% of cases (35 of 93). The median advantage of correct disease suggestions compared to the time of clinical diagnosis was 3 months or 50% for top five fit and 1 month or 21% for top fit. The correct diagnosis was suggested at the first documented patient visit in 33.3% (top 5 fit), and 16.1% of cases (top fit), respectively. Wilcoxon signed-rank test shows a significant difference between the time to clinical diagnosis and the time to correct disease suggestion for both top five fit and top fit (z-score -6.68, respective -5.71, α=0.05, p-value <0.001).

CONCLUSION

Ada DX provided accurate rare disease suggestions in most rare disease cases. In many cases, Ada DX provided correct rare disease suggestions early in the course of the disease, sometimes at the very beginning of a patient journey. The interpretation of these results indicates that Ada DX has the potential to suggest rare diseases to physicians early in the course of a case. Limitations of this study derive from its retrospective and unblinded design, data input by a single user, and the optimization of the knowledge base during the course of the study. Results pertaining to the system's accuracy should be interpreted cautiously. Whether the use of Ada DX reduces the time to diagnosis in rare diseases in a clinical setting should be validated in prospective studies.

Orthodontically Relevant Manifestations in People with Rare Diseases

BACKGROUND

Approximately 15% of all rare diseases occur with orofacial manifestations. Symptoms and manifestations of relevance to orthodontists represent a considerable proportion of these diseases and require appropriate strategies for their treatment. This article provides an overview of the orthodontically relevant manifestations of rare diseases.

MATERIAL AND METHODS

Overall, 3,639 rare diseases listed at the Orphanet, OMIM or Pubmed database were evaluated for orofacial manifestations. All rare diseases which were indicated with at least one orofacial manifestation were recorded in a database for rare diseases with orofacial manifestations called "ROMSE," which was developed by the authors. All the rare diseases were analysed with regard to orthodontically relevant orofacial manifestations, such as dysgnathia, changes in the number of teeth, failures of eruption, pathologies of bone metabolism or orofacial clefts. For all rare diseases with orthodontic relevance, an exact analysis was undertaken.

RESULTS

The orthodontically relevant orofacial manifestation termed dysgnathia is described in 151 of 535 identified rare diseases (28.2%). In these 151 rare diseases, 15 different subforms of dysgnathia, in the sense of skeletal misdevelopments of the jaws but without dental abnormalities, were described. Also changes in the number of teeth (17.9%), orofacial clefts (27.6%), failures of eruption (8.4%) and pathologies of the bone (2.1%) were described.

CONCLUSIONS

Orthodontics play an important role in the diagnosis and treatment of orofacial manifestations in rare diseases. Databases such as ROMSE are a first step toward providing valid information in publicly accessible databases.