The cost of the diagnostic odyssey of patients with suspected rare diseases
Open accessAbstract
PURPOSE: Patients with rare diseases often undergo a long diagnostic odyssey. However, there is little empirical evidence on the cost incurred during the diagnostic pathway for patients with suspected rare diseases. This study provides a comprehensive analysis of healthcare costs and utilization during the diagnostic pathway for a heterogeneous sample of patients with suspected rare diseases but unclear diagnosis. METHODS: Using claims data from five German statutory health insurance organizations for the years 2014-2019, we analyzed costs and healthcare utilization of 1,243 patients (aged 0 to 82 years) with suspected rare diseases referred to a rare disease center. A control cohort was assigned using 1:75 exact matching on age, sex and place of residence. RESULTS: In the years prior to referral to an expert center, healthcare utilization of patients with suspected rare diseases was, on average, substantially and significantly higher compared to a matched control cohort during the same observation period - e.g. in terms of the number of hospitalizations (3.1 (95%CI: 2.9-3.4) vs. 0.5 (95%CI: 0.5-0.5)), different diagnoses (50.0 (95%CI: 48.1-51.9) vs. 26.4 (95%CI: 26.2-26.5)), different active substances prescribed (12.7 (95%CI: 12.2-13.3) vs. 8.2 (95%CI: 8.2-8.3)) and the number of genetic tests (14.7 (95%CI: 12.6-16.7) vs. 0.3 (95%CI: 0.3-0.3)). We found evidence of heterogeneity in utilization by age and sex. On average, direct costs (inpatient, outpatient and prescription drug costs) of patients with suspected rare diseases during the diagnostic pathway were 7.6-fold higher than the costs of matched controls (€26,999 (95%CI: €23,751 - 30,247) vs. €3,561 (95% CI: € 3,455-3,667)). Inpatient costs were the main cost component, accounting for 62.5% of total costs. CONCLUSIONS: The diagnostic odyssey of patients with suspected rare diseases is associated with extensive healthcare utilization and high cost. Against this background, new ways to shorten the diagnostic journey have a high potential to decrease the financial burden related to rare diseases.
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For patients with rare, undiagnosed, or complex diseases, MAS has the potential to mitigate the heavy financial burden offsetting computational costs through the reduction of redundant secondary testing and acute care utilization.
“For patients with rare, undiagnosed, or complex diseases, MAS has the potential to mitigate the heavy financial burden offsetting computational costs through the reduction of redundant secondary testing and acute care utilization [41].”
From A Review of Multi-Agent AI Systems for Biological and Clinical Data Analysis (2026) · cites this paper as [41]
The Results (Cost analysis; page unavailable) document substantially higher inpatient and outpatient costs for patients with suspected rare diseases. The Discussion says diagnostic support systems may reduce diagnostic delays and costs, but the paper does not address MAS, computational costs, or whether reducing redundant secondary testing and acute care utilization would offset those costs. (LLM, conf=0.87)
Experimental — accuracy not yet measuredFor patients with suspected rare diseases, uncoordinated clinical investigations typically incur costs 7.6-fold higher than matched controls.
“For patients with suspected rare diseases, uncoordinated clinical investigations typically incur costs 7.6-fold higher than matched controls [41].”
From A Review of Multi-Agent AI Systems for Biological and Clinical Data Analysis (2026) · cites this paper as [41]
The Abstract Results and Conclusion report that mean direct healthcare costs during the diagnostic pathway were 7.6-fold higher for patients with suspected rare diseases than for matched controls. They do not establish that the investigations were uncoordinated or that the 7.6-fold difference was specifically the cost of clinical investigations; the measure includes inpatient, outpatient, and prescription drug costs. (LLM, conf=0.89)
Experimental — accuracy not yet measuredRecent systematic reviews of 39 medical AI benchmarks quantify a significant 'knowledge-practice gap', finding that models achieving 84-90% on examinations often drop to 45-69% success rates when subjected to practice-based assessments requiring diagnostic reasoning under uncertainty.
“Recent systematic reviews of 39 medical AI benchmarks quantify a significant ‘knowledge-practice gap,’ finding that models achieving 84–90% on examinations often drop to 45–69% success rates when subjected to practice-based assessments that require diagnostic reasoning under uncertainty [41].”
From A Review of Multi-Agent AI Systems for Biological and Clinical Data Analysis (2026) · cites this paper as [41]
Split into 2 atoms — 0 confirmed, 0 partially supported, 0 disputed, 0 misattributed, 2 unverifiable. Expand the claim to see per-atom verdicts.
Experimental — accuracy not yet measuredA fundamental 'knowledge-practice gap' remains a bottleneck for clinical adoption of MAS.
“Beyond reporting bias, a fundamental ‘knowledge-practice gap’ remains a bottleneck for clinical adoption of MAS [41].”
From A Review of Multi-Agent AI Systems for Biological and Clinical Data Analysis (2026) · cites this paper as [41]
The Methods section describes a study of diagnostic pathways and costs for patients with suspected rare diseases; the Discussion addresses limitations of that study. Searches for “knowledge-practice gap” and barriers to clinical adoption of MAS found no passage addressing the claimed bottleneck. (LLM, conf=0.76)
Experimental — accuracy not yet measuredThis disparity suggests that current models suffer from 'inflexible reasoning,' or the Einstellung effect, where they rely on pattern matching from training data rather than engaging in adaptive, threshold-based decision-making required at the bedside.
“This disparity suggests that current models suffer from “inflexible reasoning,” or the Einstellung effect, where they rely on pattern matching from training data rather than engaging in the adaptive, threshold-based decision-making required at the bedside [41,68].”
From A Review of Multi-Agent AI Systems for Biological and Clinical Data Analysis (2026) · cites this paper as [41]
Split into 2 atoms — 0 confirmed, 0 partially supported, 0 disputed, 0 misattributed, 2 unverifiable. Expand the claim to see per-atom verdicts.
Experimental — accuracy not yet measured
In low-risk environments, the marginal gains of multi-agent deliberation do not offset the increased latency, API costs, or computational footprint.
“In these low-risk environments, the marginal gains do not offset the increased latency, API costs, or computational footprint [41,55].”
From A Review of Multi-Agent AI Systems for Biological and Clinical Data Analysis (2026) · cites this paper as [41]
The Results and Discussion address healthcare costs during diagnosis of suspected rare diseases, not multi-agent deliberation. Searches for low-risk environments, latency, API costs, and computational footprint found no passage addressing the claimed trade-off. Page numbers were not available in our copy. (LLM, conf=0.78)
Experimental — accuracy not yet measuredA 2025 systematic review of 39 medical LLM benchmarks quantified a 19.3 percentage point drop in success rates when moving from examination-based pattern recognition (84-90%) to practice-based assessments (45-69%).
“A 2025 systematic review of 39 medical LLM benchmarks quantified a 19.3 percentage point drop in success rates when moving from examination-based pattern recognition (84–90%) to practice-based assessments (45–69%) [41].”
From A Review of Multi-Agent AI Systems for Biological and Clinical Data Analysis (2026) · cites this paper as [41]
The Study population and Diagnostic pathways sections describe patients with suspected rare diseases, matched controls, and diagnostic procedures—not a review of medical LLM benchmarks. Searches for the 39 benchmarks, the 19.3-point drop, and the stated performance ranges found no passage addressing the claim. (LLM, conf=0.78)
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Cites
Bundesgesundheitsblatt - Gesundheitsforschung - Gesundheitsschutz
(2013) · Bundesgesundheitsblatt - Gesundheitsforschung - Gesundheitsschutz · doi:10.1007/103.1437-1588 · Cited by 11,769 · see citing works
Statistical tests, P values, confidence intervals, and power: a guide to misinterpretations
Greenland, S., Senn, S. J., Rothman, K. J., Carlin, J. B., Poole, C., Goodman, S. N., & Altman, D. G. (2016) · European Journal of Epidemiology · doi:10.1007/s10654-016-0149-3 · Cited by 3,200 · see citing works
Estimating cumulative point prevalence of rare diseases: analysis of the Orphanet database
Nguengang Wakap, S., Lambert, D. M., Olry, A., Rodwell, C., Gueydan, C., Lanneau, V., Murphy, D., Le Cam, Y., & Rath, A. (2020) · European Journal of Human Genetics · doi:10.1038/s41431-019-0508-0 · Cited by 1,752 · see citing works
Ferreira, C. R. (2019) · American Journal of Medical Genetics Part A · doi:10.1002/ajmg.a.61124 · Cited by 508 · see citing works
Sources of Geographic Variation in Health Care: Evidence From Patient Migration*
Finkelstein, A., Gentzkow, M., & Williams, H. (2016) · The Quarterly Journal of Economics · doi:10.1093/qje/qjw023 · Cited by 500 · see citing works
‘I just want permission to be ill’: Towards a sociology of medically unexplained symptoms
Nettleton, S. (2005) · Social Science & Medicine · doi:10.1016/j.socscimed.2005.07.030 · Cited by 480 · see citing works
Confidence Interval or P-Value? Part 4 of a Series on Evaluation of Scientific Publications
du Prel, J., Hommel, G., Röhrig, B., & Blettner, M. (2009) · Deutsches Ärzteblatt international · doi:10.3238/arztebl.2009.0335 · Cited by 460 · see citing works
Effect of Genetic Diagnosis on Patients with Previously Undiagnosed Disease
Splinter, K., Adams, D. R., Bacino, C. A., Bellen, H. J., Bernstein, J. A., Cheatle-Jarvela, A. M., Eng, C. M., Esteves, C., Gahl, W. A., Hamid, R., et al. (2018) · New England Journal of Medicine · doi:10.1056/nejmoa1714458 · Cited by 359 · see citing works
Tan, T. Y., Dillon, O. J., Stark, Z., SCHOFIELD, D. J., Alam, K., Shrestha, R. N., Chong, B., Phelan, D., Brett, G. R., Creed, E., et al. (2017) · Archives of Pediatrics and Adolescent Medicine · doi:10.1001/jamapediatrics.2017.1755 · Cited by 325 · see citing works
Socio-economic burden of rare diseases: A systematic review of cost of illness evidence
Angelis, A., Tordrup, D., & Kanavos, P. G. (2014) · Health Policy · doi:10.1016/j.healthpol.2014.12.016 · Cited by 317 · see citing works
Stark, Z., SCHOFIELD, D. J., Alam, K., Wilson, W., Mupfeki, N., Macciocca, I., Shrestha, R. N., White, S. M., & Gaff, C. (2017) · Genetics in Medicine · doi:10.1038/gim.2016.221 · Cited by 238 · see citing works
Survey of healthcare experiences of Australian adults living with rare diseases
Molster, C. M., Urwin, D., Pietro, L. D., Fookes, M., Petrie, D., van der Laan, S., & Dawkins, H. J. S. (2016) · Orphanet Journal of Rare Diseases · doi:10.1186/s13023-016-0409-z · Cited by 215 · see citing works
Zurynski, Y., Deverell, M., Dalkeith, T., Johnson, S., Christodoulou, J., Leonard, H., & Elliott, E. (2017) · Orphanet Journal of Rare Diseases · doi:10.1186/s13023-017-0622-4 · Cited by 211 · see citing works
Monroe, G. R., Frederix, G., Savelberg, S. M. C., de Vries, T. I., Duran, K. J., van der Smagt, J. J., Terhal, P. A., van Hasselt, P. M., Kroes, H. Y., Verhoeven‐Duif, N. M., et al. (2016) · Genetics in Medicine · doi:10.1038/gim.2015.200 · Cited by 188 · see citing works
Stark, Z., SCHOFIELD, D. J., Martyn, M. M., Rynehart, L., Shrestha, R. N., Alam, K., Lunke, S., Tan, T. Y., Gaff, C., & White, S. M. (2018) · Genetics in Medicine · doi:10.1038/s41436-018-0006-8 · Cited by 173 · see citing works
Faye, F. M., Crocione, C., de Peña, R. A., Bellagambi, S., Peñaloza, L. E., Hunter, A., Jensen, L., Oosterwijk, C., Schoeters, E., de Vicente, D., et al. (2024) · European Journal of Human Genetics · doi:10.1038/s41431-024-01604-z · Cited by 157 · see citing works
Ronicke, S., Hirsch, M. C., Türk, E., Larionov, K., Tientcheu, D., & Wagner, A. D. (2019) · Orphanet Journal of Rare Diseases · doi:10.1186/s13023-019-1040-6 · Cited by 134 · see citing works
The diagnostic odyssey: insights from parents of children living with an undiagnosed condition
Bauskis, A., Strange, C., Molster, C. M., & Fisher, C. M. (2022) · Orphanet Journal of Rare Diseases · doi:10.1186/s13023-022-02358-x · Cited by 130 · see citing works
Tisdale, A., Cutillo, C. M., Nathan, R., Russo, P. A., Laraway, B. J., Haendel, M. A., Nowak, D., Hasche, C., Chan, C., Griese, E. R., et al. (2021) · Orphanet Journal of Rare Diseases · doi:10.1186/s13023-021-02061-3 · Cited by 106 · see citing works
Sickle Cell Disease–Related Pediatric Medical Expenditures in the U.S.
Amendah, D. D., Mvundura, M., Kavanagh, P. L., Sprinz, P. G., & Grosse, S. D. (2010) · American Journal of Preventive Medicine · doi:10.1016/j.amepre.2010.01.004 · Cited by 89 · see citing works
Boulet, S. L., Molinari, N., Grosse, S. D., Honein, M. A., & Correa, A. (2008) · The Journal of Pediatrics · doi:10.1016/j.jpeds.2008.02.046 · Cited by 83 · see citing works
Nurchis, M. C., Radio, F. C., Salmasi, L., Alizadeh, A. H., Raspolini, G. M., Altamura, G., Tartaglia, M., Dallapiccola, B., Pizzo, E., Gianino, M. M., et al. (2024) · JAMA Network Open · doi:10.1001/jamanetworkopen.2023.53514 · Cited by 67 · see citing works
Diagnostic delays in vasculitis and factors associated with time to diagnosis
Sreih, A. G., Cronin, K., Shaw, D. G., Young, K., Burroughs, C., Kullman, J. A., Machireddy, K., McAlear, C. A., & Merkel, P. A. (2021) · Orphanet Journal of Rare Diseases · doi:10.1186/s13023-021-01794-5 · Cited by 63 · see citing works
Diagnostic delay in rare diseases: data from the Spanish rare diseases patient registry
Benito-Lozano, J., López-Villalba, B., Arias-Merino, G., de la Paz, M. P., & Alonso-Ferreira, V. (2022) · Orphanet Journal of Rare Diseases · doi:10.1186/s13023-022-02530-3 · Cited by 53 · see citing works
Incerti, D., Xu, X.-M., Chou, J. W., Gonzaludo, N., Belmont, J. W., & Schroeder, B. E. (2021) · Genetics in Medicine · doi:10.1016/j.gim.2021.08.015 · Cited by 50 · see citing works
Rare diseases: why is a rapid referral to an expert center so important?
Willmen, T., Willmen, L., Pankow, A., Ronicke, S., Gabriel, H., & Wagner, A. D. (2023) · BMC Health Services Research · doi:10.1186/s12913-023-09886-7 · Cited by 49 · see citing works
Cost-of-illness studies in rare diseases: a scoping review
García‐Pérez, L., Linertová, R., Valcárcel‐Nazco, C., de la Paz, M. P., Gorostiza, I., & Serrano-Aguilar, P. G. (2021) · Orphanet Journal of Rare Diseases · doi:10.1186/s13023-021-01815-3 · Cited by 45 · see citing works
Socio-economic costs of rare diseases and the risk of financial hardship: a cross-sectional study
Chung, C. C. Y., Ng, N. Y. T., Ng, Y. N. C., Lui, A. C. Y., Fung, J. L. F., Chan, M. C. Y., Wong, W. H. S., Lee, S. L., Knapp, M. R. J., & Chung, B. H. (2023) · The Lancet Regional Health - Western Pacific · doi:10.1016/j.lanwpc.2023.100711 · Cited by 42 · see citing works
The cost trajectory of the diagnostic care pathway for children with suspected genetic disorders
Dragojlovic, N., van Karnebeek, C., Ghani, A., Genereaux, D., Kim, E., Birch, P. H., Adam, S., Dragojlovic, N., du Souich, C., Elliott, A. M., et al. (2019) · Genetics in Medicine · doi:10.1038/s41436-019-0635-6 · Cited by 41 · see citing works
Michaels-Igbokwe, C., McInnes, B. L., MacDonald, K. V., Currie, G. R., Omar, F., Shewchuk, B., Bernier, F. P., & Marshall, D. A. (2020) · Genetics in Medicine · doi:10.1038/s41436-020-00975-0 · Cited by 39 · see citing works
Hümmert, M. W., Schöppe, L. M., Bellmann-Strobl, J., Siebert, N., Paul, F., Duchow, A., Pellkofer, H., Kümpfel, T., Havla, J., Jarius, S., et al. (2022) · Neurology · doi:10.1212/wnl.0000000000200052 · Cited by 39 · see citing works
Sources of regional variation in healthcare utilization in Germany
Salm, M., & Wübker, A. (2019) · Journal of Health Economics · doi:10.1016/j.jhealeco.2019.102271 · Cited by 37 · see citing works
Survival of patients with rare diseases: a population-based study in Tuscany (Italy)
Gorini, F., Coi, A., Mezzasalma, L., Baldacci, S., Pierini, A., & Santoro, M. C. (2021) · Orphanet Journal of Rare Diseases · doi:10.1186/s13023-021-01907-0 · Cited by 34 · see citing works
The interdisciplinary diagnosis of rare diseases—results of the Translate-NAMSE project
Rillig, F., Grüters, A., Schramm, C., & Krude, H. (2022) · Deutsches Ärzteblatt international · doi:10.3238/arztebl.m2022.0219 · Cited by 32 · see citing works
Health economic benefits through the use of diagnostic support systems and expert knowledge
Willmen, T., Völkel, L., Ronicke, S., Hirsch, M. C., Kaufeld, J. K., Rychlik, R. P. T., & Wagner, A. D. (2021) · BMC Health Services Research · doi:10.1186/s12913-021-06926-y · Cited by 30 · see citing works
Estimating mortality in rare diseases using a population-based registry, 2002 through 2019
Mazzucato, M., Pozza, L. V. D., Minichiello, C., Toto, E., Vianello, A., & Facchin, P. (2023) · Orphanet Journal of Rare Diseases · doi:10.1186/s13023-023-02944-7 · Cited by 28 · see citing works
Costs of the diagnostic odyssey in children with inherited leukodystrophies
Richards, J. R., Korgenski, E. K., Srivastava, R., & Bonkowsky, J. L. (2015) · Neurology · doi:10.1212/wnl.0000000000001974 · Cited by 27 · see citing works
Choukair, D., Hauck, F. H., Bettendorf, M., Krude, H., Klein, C., Bäumer, T., Berner, R., Lee‐Kirsch, M. A., Grasemann, C., Burgard, P., et al. (2021) · Orphanet Journal of Rare Diseases · doi:10.1186/s13023-021-02092-w · Cited by 25 · see citing works
International Undiagnosed Diseases Programs (UDPs): components and outcomes
Curic, E., Ewans, L., Pysar, R., Taylan, F., Botto, L. D., Nordgren, A., Gahl, W. A., & Palmer, E. E. (2023) · Orphanet Journal of Rare Diseases · doi:10.1186/s13023-023-02966-1 · Cited by 15 · see citing works
Developing a Framework of Cost Elements of Socioeconomic Burden of Rare Disease: A Scoping Review
Currie, G. R., Gerber, B., Lorenzetti, D., MacDonald, K. V., Benseler, S. M., Bernier, F. P., Boycott, K. M., Carias, K. V., Hamelin, B., Hayeems, R. Z., et al. (2023) · PharmacoEconomics · doi:10.1007/s40273-023-01262-x · Cited by 15 · see citing works
Seltene Erkrankungen in den Daten sichtbar machen – Kodierung
Martin, T., Rommel, K., Thomas, C., Eymann, J., Kretschmer, T., Berner, R., Lee‐Kirsch, M. A., & Hebestreit, H. U. (2022) · Bundesgesundheitsblatt - Gesundheitsforschung - Gesundheitsschutz · doi:10.1007/s00103-022-03598-9 · Cited by 12 · see citing works
Marx, M. M., Dulas, F. M., & Schumacher, K. M. (2017) · Bundesgesundheitsblatt - Gesundheitsforschung - Gesundheitsschutz · doi:10.1007/s00103-017-2534-9 · Cited by 10 · see citing works
Druschke, D., Krause, F., Müller, G., Scharfe, J., Hoffmann, G. F., & Schmitt, J. M. (2021) · Orphanet Journal of Rare Diseases · doi:10.1186/s13023-021-02106-7 · Cited by 10 · see citing works
Die Berechnung indikationsspezifischer Kosten bei GKV-Routinedatenanalysen am Beispiel von ADHS
Zeidler, J., Lange, A., Braun, S., Linder, R., Engel, S., Verheyen, F., & von der Schulenburg, J. M. G. (2013) · Bundesgesundheitsblatt - Gesundheitsforschung - Gesundheitsschutz · doi:10.1007/s00103-012-1624-y · Cited by 5 · see citing works
Introduction of ICD-11 in Germany: Seizing opportunities together
Broich, K., Callhoff, J., Kaskel, P., Kowalski, C., Malzahn, J., Mundlos, C., & Schöbel, C. (2024) · Das Gesundheitswesen · doi:10.1055/a-2342-4453 · Cited by 4 · see citing works
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- Authors
- Glaubitz, R., Heinrich, L., Tesch, F., Seifert, M., Reber, K. C., Marschall, U., Schmitt, J., & Müller, G.
- Affiliations
- University Hospital Carl Gustav Carus (DE)
- Corresponding author
- Glaubitz, Rick
- ORCID
- Venue
- Orphanet Journal of Rare Diseases
- Published
- 2025-05-10
- Volume
- 20
- Issue
- 1
- Pages
- 222-222
- ISSN
- 1750-1172 (ISSN-L)
- PMC
- PMC12065212
- OpenAlex
- W4410250231
- Cited by
- 17
- References
- 44
- Citations by year
- 2026: 15 · 2025: 2
- FWCI
- 9.99
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Orphanet Journal of Rare Diseases (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Technische Universität Dresden
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genomics and Rare Diseases
- Health Sciences › Health Professions › General Health Professions › Healthcare cost, quality, practices
- Social Sciences › Economics, Econometrics and Finance › Economics and Econometrics › Healthcare Policy and Management
- Keywords
- rare diseases, diagnostic odyssey, diagnostic pathway, health care costs, health care utilization, hospitalization, genetic testing, rare disease centers, hospital costs, prescription drug costs, age and sex differences, Germany, statutory health insurance claims data, unmet medical needs, delayed diagnosis, quality of life
- MeSH terms
- Adolescent, Adult, Aged, Aged, 80 and over, Child, Child, Preschool, Female, Germany, Hospitalization, Humans, Infant, Infant, Newborn, Male, Middle Aged, Health Care Costs, Rare Diseases, Young Adult
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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