{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,27]],"date-time":"2026-02-27T04:20:11Z","timestamp":1772166011608,"version":"3.50.1"},"reference-count":71,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,11,26]],"date-time":"2025-11-26T00:00:00Z","timestamp":1764115200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2025,12,30]],"date-time":"2025-12-30T00:00:00Z","timestamp":1767052800000},"content-version":"vor","delay-in-days":34,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["BMC Med Inform Decis Mak"],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Rare disease diagnosis often involves complex, lengthy, and costly procedures. Traditional cost-effectiveness analyses typically rely on static diagnostic workflow models that apply uniform diagnostic strategies across heterogeneous patient populations. With recent advancements in artificial intelligence (AI) and a growing emphasis on personalized medicine, there is a pressing need for dynamic frameworks that assess diagnostic cost-effectiveness at the individual patient level.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>\n                      We introduce the PRICE analysis framework, a novel, tree-based model designed to evaluate the cost-effectiveness of diagnostic strategies, accommodating both expert-alone and AI-delegated decision-making modes. The model computes the expected cost of a diagnostic process via a back-propagation algorithm and quantifies effectiveness through a utility-based approach (i.e.,\n                      <jats:italic>Quality Adjusted Life Years<\/jats:italic>\n                      ). Parameters such as disease prevalence, test costs, test performance metrics, and turnaround time are incorporated to enable individualized assessments.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>We demonstrat the utility of this novel framework in a proof-of-concept study by evaluating four diagnostic strategies for developmental delay (DD) and multiple congenital anomalies (MCA). The results highlight how PRICE can support personalized decision-making by modeling outcomes under varying parameters such as cost, prevalence, yield, and AI accuracy. To better visualize and interpret this framework, we developed an interactive web-based tool to demonstrate how to build PRICE pathways and conduct cost-effectiveness analysis in real time.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>PRICE is a novel cost-effective analysis framework that captures the sequential and recursive nature of real-world diagnostic workflows, with the ability to be extended to future AI-integrated clinical practice. It enables personalized evaluations of diagnostic strategies from both economic and clinical perspectives, promoting more informed and individualized decision-making for rare disease diagnosis.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1186\/s12911-025-03277-0","type":"journal-article","created":{"date-parts":[[2025,11,26]],"date-time":"2025-11-26T19:15:14Z","timestamp":1764184514000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["PRICE: a personalized recursive intelligent cost effectiveness analysis framework for rare disease diagnosis"],"prefix":"10.1186","volume":"25","author":[{"given":"Mengshu","family":"Nie","sequence":"first","affiliation":[]},{"given":"Yujing","family":"Yao","sequence":"additional","affiliation":[]},{"given":"Junyoung","family":"Kim","sequence":"additional","affiliation":[]},{"given":"Cong","family":"Liu","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,11,26]]},"reference":[{"issue":"2","key":"3277_CR1","doi-asserted-by":"publisher","first-page":"29","DOI":"10.1007\/s12325-019-01176-1","volume":"37","author":"F Ramos-Fuentes","year":"2020","unstructured":"Ramos-Fuentes F, Gonz\u00e1lez-Meneses A, Ars E, Hern\u00e1ndez-Jaras J. Genetic diagnosis of rare diseases: past and present. Adv Ther. 2020;37 (Suppl 2):29\u201337. https:\/\/doi.org\/10.1007\/s12325-019-01176-1","journal-title":"Adv Ther"},{"key":"3277_CR2","doi-asserted-by":"publisher","first-page":"322","DOI":"10.20517\/jtgg.2022.03","volume":"6","author":"D Casas-Alba","year":"2022","unstructured":"Casas-Alba D, Hoenicka J, Vilanova-Adell A, Vega-Hanna L, Pijuan J, Palau F. Diagnostic strategies in patients with undiagnosed and rare diseases. J Transl Genet Genomics. 2022;6:322\u201332. https:\/\/doi.org\/10.20517\/jtgg.2022.03","journal-title":"J Transl Genet Genomics"},{"key":"3277_CR3","doi-asserted-by":"publisher","first-page":"639","DOI":"10.2471\/BLT.16.187468","volume":"95","author":"CS Kosack","year":"2017","unstructured":"Kosack CS, Page A-L, Klatser PR. A guide to aid the selection of diagnostic tests. Bull. World Health Organ. 2017;95:639\u201345. https:\/\/doi.org\/10.2471\/BLT.16.187468","journal-title":"Bull. World Health Organ"},{"key":"3277_CR4","doi-asserted-by":"publisher","first-page":"8","DOI":"10.1186\/s41512-024-00175-3","volume":"8","author":"TR Fanshawe","year":"2024","unstructured":"Fanshawe TR, Nicholson BD, Perera R, Oke JL. A review of methods for the analysis of diagnostic tests performed in sequence. Diagn Progn Res. 2024;8:8. https:\/\/doi.org\/10.1186\/s41512-024-00175-3","journal-title":"Diagn Progn Res"},{"key":"3277_CR5","doi-asserted-by":"publisher","first-page":"1248","DOI":"10.1016\/j.jclinepi.2009.01.008","volume":"62","author":"JD Schaafsma","year":"2009","unstructured":"Schaafsma JD, van der GY, Rinkel GJE, Buskens E. Decision analysis to complete diagnostic research by closing the gap between test characteristics and cost-effectiveness. J Clin Epidemiol. 2009;62:1248\u201352. https:\/\/doi.org\/10.1016\/j.jclinepi.2009.01.008","journal-title":"J Clin Epidemiol"},{"key":"3277_CR6","doi-asserted-by":"publisher","first-page":"ii11","DOI":"10.1136\/bmjqs-2012-001616","volume":"22 Suppl 2","author":"DE Newman-Toker","year":"2013","unstructured":"Newman-Toker DE, McDonald KM, Meltzer DO. How much diagnostic safety can we afford, and how should we decide? A health economics perspective. BMJ Qual Saf. 2013;22 Suppl 2:ii11\u201320. https:\/\/doi.org\/10.1136\/bmjqs-2012-001616","journal-title":"BMJ Qual Saf"},{"key":"3277_CR7","doi-asserted-by":"publisher","unstructured":"Fu R, Ng V, Liu M, Wells D, Yurga E, Nauenberg E. Considering patient perspectives in economic evaluations of health interventions. Front Public Health. 2023;11. https:\/\/doi.org\/10.3389\/fpubh.2023.1212583","DOI":"10.3389\/fpubh.2023.1212583"},{"key":"3277_CR8","doi-asserted-by":"publisher","first-page":"451","DOI":"10.1038\/s41436-020-01012-w","volume":"23","author":"C Li","year":"2021","unstructured":"Li C, Vandersluis S, Holubowich C, Ungar WJ, Goh ES, Boycott KM, et al. Cost-effectiveness of genome-wide sequencing for unexplained developmental disabilities and multiple congenital anomalies. Genet Med. 2021;23:451\u201360. https:\/\/doi.org\/10.1038\/s41436-020-01012-w","journal-title":"Genet Med"},{"key":"3277_CR9","doi-asserted-by":"publisher","first-page":"334","DOI":"10.5530\/ijpi.20250100","volume":"15","author":"LP Nori","year":"2025","unstructured":"Nori LP, Lohitha M, Vadapalli RR, Bonthagarala B, Nagineni SR, Kalidindi VR. Revolutionizing healthcare: the impact of AI on precision medicine. Int J Pharm Investig. 2025;15:334\u201343. https:\/\/doi.org\/10.5530\/ijpi.20250100","journal-title":"Int J Pharm Investig"},{"key":"3277_CR10","unstructured":"Camidge R. Personalized medicine: tailoring diagnoses to individual patients"},{"key":"3277_CR11","doi-asserted-by":"publisher","first-page":"205","DOI":"10.1136\/qhc.12.3.205","volume":"12","author":"R Foy","year":"2003","unstructured":"Foy R, Warner P. About time: diagnostic guidelines that help clinicians. BMJ Qual Saf. 2003;12:205\u201309. https:\/\/doi.org\/10.1136\/qhc.12.3.205","journal-title":"BMJ Qual Saf"},{"key":"3277_CR12","doi-asserted-by":"publisher","first-page":"319","DOI":"10.1186\/s12859-023-05443-5","volume":"24","author":"PK Ata\u015f","year":"2023","unstructured":"Ata\u015f PK. A novel hybrid model to predict concomitant diseases for Hashimoto\u2019s thyroiditis. BMC Bioinf. 2023;24:319. https:\/\/doi.org\/10.1186\/s12859-023-05443-5","journal-title":"BMC Bioinf"},{"key":"3277_CR13","doi-asserted-by":"publisher","first-page":"34","DOI":"10.1007\/s13755-025-00350-w","volume":"13","author":"K Ata\u015f P","year":"2025","unstructured":"Ata\u015f P K. A novel clustered-based binary grey wolf optimizer to solve the feature selection problem for uncovering the genetic links between non-Hodgkin lymphomas and rheumatologic diseases. Health Inf Sci Syst. 2025;13:34. https:\/\/doi.org\/10.1007\/s13755-025-00350-w","journal-title":"Health Inf Sci Syst"},{"key":"3277_CR14","doi-asserted-by":"publisher","first-page":"110343","DOI":"10.1016\/j.compbiomed.2025.110343","volume":"193","author":"PK Ata\u015f","year":"2025","unstructured":"Ata\u015f PK. A novel Harris Hawks optimization-based clustering method for elucidating genetic associations in osteoarthritis and diverse cancer types. Comput Biol Med. 2025;193:110343. https:\/\/doi.org\/10.1016\/j.compbiomed.2025.110343","journal-title":"Comput Biol Med"},{"key":"3277_CR15","doi-asserted-by":"publisher","first-page":"295","DOI":"10.3390\/math12020295","volume":"12","author":"K Ata\u015f P","year":"2024","unstructured":"Ata\u015f P K. Exploring the molecular interaction of PCOS and Endometrial carcinoma through novel hyperparameter-optimized ensemble clustering approaches. Mathematics. 2024;12:295. https:\/\/doi.org\/10.3390\/math12020295","journal-title":"Mathematics"},{"key":"3277_CR16","doi-asserted-by":"publisher","first-page":"2347","DOI":"10.1007\/s11590-021-01816-y","volume":"17","author":"P Karadayi-Ata\u015f","year":"2023","unstructured":"Karadayi-Ata\u015f P, Sevkli AZ, Tufan K. A VNS based framework for early diagnosis of the Alzheimer\u2019s disease converted from mild cognitive impairment. Optim Lett. 2023;17:2347\u201366. https:\/\/doi.org\/10.1007\/s11590-021-01816-y","journal-title":"Optim Lett"},{"key":"3277_CR17","doi-asserted-by":"publisher","first-page":"228049","DOI":"10.1109\/ACCESS.2020.3042273","volume":"8","author":"S Kaur","year":"2020","unstructured":"Kaur S, Singla J, Nkenyereye L, Jha S, Prashar D, Joshi GP, et al. Medical diagnostic systems using artificial intelligence (ai) algorithms: principles and perspectives. IEEE Access. 2020;8:228049\u201369. https:\/\/doi.org\/10.1109\/ACCESS.2020.3042273","journal-title":"IEEE Access"},{"key":"3277_CR18","doi-asserted-by":"publisher","first-page":"39","DOI":"10.1038\/s41746-018-0040-6","volume":"1","author":"MD Abr\u00e0moff","year":"2018","unstructured":"Abr\u00e0moff MD, Lavin PT, Birch M, Shah N, Folk JC. Pivotal trial of an autonomous AI-based diagnostic system for detection of diabetic retinopathy in primary care offices. NPJ Digit Med. 2018;1:39. https:\/\/doi.org\/10.1038\/s41746-018-0040-6","journal-title":"NPJ Digit Med"},{"key":"3277_CR19","first-page":"3898","volume":"66","author":"B Poschkamp","year":"2025","unstructured":"Poschkamp B, Kantz L, Augstein P, Tayar A, Br\u00fcnder M-C, Kerner W, et al. Real-world challenges in AI-Based diabetic retinopathy screening: insights from non-mydriatic imaging and required adjustments. Invest Ophthalmol Vis Sci. 2025;66:3898","journal-title":"Invest Ophthalmol Vis Sci"},{"key":"3277_CR20","doi-asserted-by":"publisher","first-page":"142","DOI":"10.2337\/cd23-0019","volume":"42","author":"RM Wolf","year":"2023","unstructured":"Wolf RM, Channa R, Lehmann HP, Abramoff MD, Liu TYA. Clinical implementation of autonomous artificial intelligence systems for diabetic eye exams: considerations for success. Clin Diabetes. 2023;42:142\u201349. https:\/\/doi.org\/10.2337\/cd23-0019","journal-title":"Clin Diabetes"},{"key":"3277_CR21","doi-asserted-by":"publisher","first-page":"205520762311865","DOI":"10.1177\/20552076231186520","volume":"9","author":"E Sezgin","year":"2023","unstructured":"Sezgin E. Artificial intelligence in healthcare: complementing, not replacing, doctors and healthcare providers. Digit Health. 2023;9:20552076231186520. https:\/\/doi.org\/10.1177\/20552076231186520","journal-title":"Digit Health"},{"key":"3277_CR22","doi-asserted-by":"publisher","first-page":"14952","DOI":"10.1038\/s41598-022-18751-2","volume":"12","author":"C Reverberi","year":"2022","unstructured":"Reverberi C, Rigon T, Solari A, Hassan C, Cherubini P, Cherubini A. Experimental evidence of effective human\u2013AI collaboration in medical decision-making. Sci Rep. 2022;12:14952. https:\/\/doi.org\/10.1038\/s41598-022-18751-2","journal-title":"Sci Rep"},{"key":"3277_CR23","doi-asserted-by":"publisher","first-page":"474","DOI":"10.1186\/s13023-021-02092-w","volume":"16","author":"D Choukair","year":"2021","unstructured":"Choukair D, Hauck F, Bettendorf M, Krude H, Klein C, B\u00e4umer T, et al. An integrated clinical pathway for diagnosis, treatment and care of rare diseases: model, operating procedures, and results of the project TRANSLATE-NAMSE funded by the German Federal joint committee. Orphanet J Rare Dis. 2021;16:474. https:\/\/doi.org\/10.1186\/s13023-021-02092-w","journal-title":"Orphanet J Rare Dis"},{"key":"3277_CR24","doi-asserted-by":"publisher","first-page":"289","DOI":"10.1007\/s11606-006-0075-2","volume":"22","author":"JB McKinlay","year":"2007","unstructured":"McKinlay JB, Link CL, Freund KM, Marceau LD, O\u2019Donnell AB, Lutfey KL. Sources of variation in Physician adherence with clinical guidelines: results from a factorial experiment. J Gen Intern Med. 2007;22:289\u201396. https:\/\/doi.org\/10.1007\/s11606-006-0075-2","journal-title":"J Gen Intern Med"},{"key":"3277_CR25","doi-asserted-by":"publisher","first-page":"1257","DOI":"10.1038\/ki.2009.92","volume":"75","author":"SK van","year":"2009","unstructured":"Stralen KJ van, Stel VS, Reitsma JB, Dekker FW, Zoccali C, Jager KJ. Diagnostic methods I: sensitivity, specificity, and other measures of accuracy. Kidney Int. 2009;75:1257\u201363. https:\/\/doi.org\/10.1038\/ki.2009.92","journal-title":"Kidney Int"},{"key":"3277_CR26","doi-asserted-by":"publisher","first-page":"65","DOI":"10.1016\/B978-0-12-741252-8.50010-8","volume-title":"Neural networks for perception","author":"R Hecht-Nielsen","year":"1992","unstructured":"Hecht-Nielsen R. Theory of the backpropagation neural Network*. In: Wechsler H, editor. Neural networks for perception. Academic Press; 1992. p. 65\u201393. https:\/\/doi.org\/10.1016\/B978-0-12-741252-8.50010-8"},{"key":"3277_CR27","doi-asserted-by":"publisher","first-page":"306","DOI":"10.1177\/0269216316689652","volume":"31","author":"AB Wichmann","year":"2017","unstructured":"Wichmann AB, Adang EM, Stalmeier PF, Kristanti S, Van den Block L, Vernooij-Dassen MJ, et al. The use of quality-adjusted life years in cost-effectiveness analyses in palliative care: mapping the debate through an integrative review. Palliat Med. 2017;31:306\u201322. https:\/\/doi.org\/10.1177\/0269216316689652","journal-title":"Palliat Med"},{"key":"3277_CR28","first-page":"1197","volume":"22","author":"C Bakker","year":"1995","unstructured":"Bakker C, van der Linden S. Health related utility measurement: an introduction. J Rheumatol. 1995;22:1197\u201399","journal-title":"J Rheumatol"},{"key":"3277_CR29","unstructured":"Integrating the healthcare ecosystem with algorithm development for accelerated impact. 2024"},{"key":"3277_CR30","unstructured":"P. Blueprint genetics. https:\/\/blueprintgenetics.com\/pricing\/. Accessed 8 May 2025"},{"key":"3277_CR31","unstructured":"Invitae chromosomal Microarray analysis (cma) | test catalog | Invitae. https:\/\/www.invitae.com\/us\/providers\/test-catalog\/test-56033. Accessed 1 May 2025"},{"key":"3277_CR32","unstructured":"Chromosomal microarray analysis (cma). Baylor genetics. https:\/\/www.baylorgenetics.com\/cma\/. Accessed 1 May 2025"},{"key":"3277_CR33","unstructured":"481797: Inheritest\u00ae 14-gene panel. https:\/\/www.labcorp.com\/content\/labcorp\/us\/en\/test-menu\/search\/test-details. Accessed 1 May 2025"},{"key":"3277_CR34","unstructured":"Genetics F. Fulgent genetics - Leader in next generation sequencing. https:\/\/www.fulgentgenetics.com\/products\/disease\/raredisease.html. Accessed 1 May 2025"},{"key":"3277_CR35","unstructured":"630999: whole exome sequencing \u2013 proband only, products of conception (poc). https:\/\/www.labcorp.com\/content\/labcorp\/us\/en\/test-menu\/search\/test-details. Accessed 1 May 2025"},{"key":"3277_CR36","doi-asserted-by":"publisher","first-page":"e25999","DOI":"10.1097\/MD.0000000000025999","volume":"100","author":"H Huang","year":"2021","unstructured":"Huang H, Wang Y, Zhang M, Lin N, An G, He D, et al. Diagnostic accuracy and value of chromosomal microarray analysis for chromosomal abnormalities in prenatal detection. Med (baltim). 2021;100:e25999. https:\/\/doi.org\/10.1097\/MD.0000000000025999","journal-title":"Med (baltim)"},{"key":"3277_CR37","doi-asserted-by":"publisher","first-page":"e2331162","DOI":"10.1001\/jamanetworkopen.2023.31162","volume":"6","author":"T Chen","year":"2023","unstructured":"Chen T, Fan C, Huang Y, Feng J, Zhang Y, Miao J, et al. Genomic sequencing as a first-tier screening test and outcomes of newborn screening. JAMA Netw Open. 2023;6:e2331162. https:\/\/doi.org\/10.1001\/jamanetworkopen.2023.31162","journal-title":"JAMA Netw Open"},{"key":"3277_CR38","doi-asserted-by":"publisher","first-page":"17052","DOI":"10.1038\/s41598-019-52000-3","volume":"9","author":"MJ McCabe","year":"2019","unstructured":"McCabe MJ, Gauthier M-E, Chan C-L, Thompson TJ, De Sousa SMC, Puttick C, et al. Development and validation of a targeted gene sequencing panel for application to disparate cancers. Sci Rep. 2019;9:17052. https:\/\/doi.org\/10.1038\/s41598-019-52000-3","journal-title":"Sci Rep"},{"key":"3277_CR39","doi-asserted-by":"publisher","first-page":"134","DOI":"10.1186\/s13059-015-0693-2","volume":"16","author":"Saudi Mendeliome Group","year":"2015","unstructured":"Saudi Mendeliome Group. Comprehensive gene panels provide advantages over clinical exome sequencing for Mendelian diseases. Genome Biol. 2015;16:134. https:\/\/doi.org\/10.1186\/s13059-015-0693-2","journal-title":"Genome Biol"},{"key":"3277_CR40","doi-asserted-by":"publisher","first-page":"00213","DOI":"10.1183\/23120541.00213-2020","volume":"6","author":"A Gileles-Hillel","year":"2020","unstructured":"Gileles-Hillel A, Mor-Shaked H, Shoseyov D, Reiter J, Tsabari R, Hevroni A, et al. Whole-exome sequencing accuracy in the diagnosis of primary ciliary dyskinesia. Erj Open Res. 2020;6:00213\u20132020. https:\/\/doi.org\/10.1183\/23120541.00213-2020","journal-title":"Erj Open Res"},{"key":"3277_CR41","unstructured":"Analytic validation of whole exome sequencing for clinical diagnostics of inherited disorders. Blueprint genetics. https:\/\/blueprintgenetics.com\/resources\/analytic-validation-whole-exome-sequencing-clinical-diagnostics-inherited-disorders\/. Accessed 1 May 2025"},{"key":"3277_CR42","doi-asserted-by":"publisher","first-page":"201","DOI":"10.5009\/gnl230272","volume":"18","author":"MK Kim","year":"2024","unstructured":"Kim MK, Rouphael C, McMichael J, Welch N, Dasarathy S. Challenges in and opportunities for electronic health record-based Data analysis and interpretation. Gut And Liver. 2024;18:201\u201308. https:\/\/doi.org\/10.5009\/gnl230272","journal-title":"Gut And Liver"},{"key":"3277_CR43","doi-asserted-by":"publisher","first-page":"143","DOI":"10.1177\/0272989X20985752","volume":"41","author":"PJ Rodriguez","year":"2021","unstructured":"Rodriguez PJ, Ward ZJ, Long MW, Austin SB, Wright DR. Applied methods for estimating transition probabilities from electronic health record Data. Med Decis Mak. 2021;41:143\u201352. https:\/\/doi.org\/10.1177\/0272989X20985752","journal-title":"Med Decis Mak"},{"key":"3277_CR44","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41746-024-01331-1","volume":"7","author":"F Chen","year":"2024","unstructured":"Chen F, Ahimaz P, Nguyen QM, Lewis R, Chung WK, Ta CN, et al. Phenotype driven molecular genetic test recommendation for diagnosing pediatric rare disorders. NPJ Digit Med. 2024;7:1\u201312. https:\/\/doi.org\/10.1038\/s41746-024-01331-1","journal-title":"NPJ Digit Med"},{"key":"3277_CR45","doi-asserted-by":"publisher","first-page":"17254","DOI":"10.1038\/s41598-023-44472-1","volume":"13","author":"Q Li","year":"2023","unstructured":"Li Q, Li Y, Zheng J, Yan X, Huang J, Xu Y, et al. Prevalence and trends of developmental disabilities among us children and adolescents aged 3 to 17 years, 2018\u20132021. Sci Rep. 2023;13:17254. https:\/\/doi.org\/10.1038\/s41598-023-44472-1","journal-title":"Sci Rep"},{"key":"3277_CR46","unstructured":"Adjusting for Inflation. https:\/\/www.stlouisfed.org\/publications\/page-one-economics\/2023\/01\/03\/adjusting-for-inflation. Accessed 30 Aug 2025"},{"key":"3277_CR47","unstructured":"How bls measures Price change for medical Care services in the consumer Price index. https:\/\/www.bls.gov\/cpi\/factsheets\/medical-care.htm. Accessed 30 Aug 2025. Bureau of Labor Statistics"},{"key":"3277_CR48","doi-asserted-by":"publisher","first-page":"307","DOI":"10.3389\/fpubh.2017.00307","volume":"5","author":"TR Sensitivity","year":"2017","unstructured":"Trevethan R. Sensitivity, specificity, and predictive values: foundations, pliabilities, and pitfalls in research and practice. Front Public Health. 2017;5:307. https:\/\/doi.org\/10.3389\/fpubh.2017.00307","journal-title":"Front Public Health"},{"key":"3277_CR49","doi-asserted-by":"publisher","first-page":"67","DOI":"10.1016\/j.vhri.2022.03.004","volume":"31","author":"M Santos","year":"2022","unstructured":"Santos M, Monteiro AL, Biz AN, Guerra A, Cramer H, Canuto V, et al. Guidelines for utility measurement for economic analysis: the Brazilian policy. Value Health Reg Issues. 2022;31:67\u201373. https:\/\/doi.org\/10.1016\/j.vhri.2022.03.004","journal-title":"Value Health Reg Issues"},{"key":"3277_CR50","doi-asserted-by":"crossref","unstructured":"Drobniewski F, Cooke M, Jordan J, Casali N, Mugwagwa T, Broda A, et al. Health-care costs and utilities. Systematic Rev, Meta-Anal And Econ Modell Of Mol Diagnostic Tests For Antibiotic Resist In Tuberc. NIHR Journals Lib. 2015","DOI":"10.3310\/hta19340"},{"key":"3277_CR51","doi-asserted-by":"publisher","first-page":"729","DOI":"10.1111\/j.1467-985X.2009.00592.x","volume":"172","author":"SG Baker","year":"2009","unstructured":"Baker SG, Cook NR, Vickers A, Kramer BS. Using relative utility curves to evaluate risk prediction. J R Stat Soc Ser A Stat Soc. 2009;172:729\u201348. https:\/\/doi.org\/10.1111\/j.1467-985X.2009.00592.x","journal-title":"J R Stat Soc Ser A Stat Soc"},{"key":"3277_CR52","doi-asserted-by":"publisher","first-page":"164","DOI":"10.1017\/s0266462300105021","volume":"17","author":"M Petticrew","year":"2001","unstructured":"Petticrew M, Sowden A, Lister-Sharp D. False-negative results in screening programs. Medical, psychological, and other implications. Int J Technol Assess Health Care. 2001;17:164\u201370. https:\/\/doi.org\/10.1017\/s0266462300105021","journal-title":"Int J Technol Assess Health Care"},{"key":"3277_CR53","doi-asserted-by":"publisher","first-page":"263","DOI":"10.1007\/s41669-023-00443-w","volume":"8","author":"LS Matza","year":"2024","unstructured":"Matza LS, Howell TA, Fung ET, Janes SM, Seiden M, Hackshaw A, et al. Health state utilities associated with false-positive cancer screening results. Pharmacoeconomics - Open. 2024;8:263\u201376. https:\/\/doi.org\/10.1007\/s41669-023-00443-w","journal-title":"Pharmacoeconomics - Open"},{"key":"3277_CR54","doi-asserted-by":"publisher","first-page":"e80767","DOI":"10.1371\/journal.pone.0080767","volume":"8","author":"D Boone","year":"2013","unstructured":"Boone D, Mallett S, Zhu S, Yao GL, Bell N, Ghanouni A, et al. Patients\u2019 & healthcare Professionals\u2019 values regarding true- & False-positive diagnosis when colorectal cancer screening by ct colonography: discrete choice experiment. PLoS One. 2013;8:e80767. https:\/\/doi.org\/10.1371\/journal.pone.0080767","journal-title":"PLoS One"},{"key":"3277_CR55","unstructured":"Tenny S, Mr H. In: StatPearls. Prevalence. Treasure Island (FL): StatPearls Publishing; 2025"},{"key":"3277_CR56","doi-asserted-by":"publisher","first-page":"221","DOI":"10.1038\/s41586-025-09227-0","volume":"644","author":"TJ Poterucha","year":"2025","unstructured":"Poterucha TJ, Jing L, Ricart RP, Adjei-Mosi M, Finer J, Hartzel D, et al. Detecting structural heart disease from electrocardiograms using AI. Nature. 2025;644:221\u201330. https:\/\/doi.org\/10.1038\/s41586-025-09227-0","journal-title":"Nature"},{"key":"3277_CR57","unstructured":"Prices for electrocardiogram, routine, with interpretation and report services | turquoise health. https:\/\/turquoise.health\/services\/electrocardiogram-routine-with-interpretation-and\/?utm_source=chatgpt.com. Accessed 30 Aug 2025"},{"key":"3277_CR58","unstructured":"Prices for echocardiography, transthoracic, with doppler services | turquoise health. https:\/\/turquoise.health\/services\/echo-transthoracic-w-doppler\/?utm_source=chatgpt.com. Accessed 30 Aug 2025"},{"key":"3277_CR59","doi-asserted-by":"publisher","first-page":"8","DOI":"10.1186\/s44156-022-00009-2","volume":"9","author":"SM Ng","year":"2022","unstructured":"Ng SM, Naqvi D, Bingcang J, Cruz G, Nose R, Lloyd G, et al. Feasibility, diagnostic performance and clinical value of an abbreviated echocardiography protocol in an out-patient cardiovascular setting: a pilot study. Echo Res Pract. 2022;9:8. https:\/\/doi.org\/10.1186\/s44156-022-00009-2","journal-title":"Echo Res Pract"},{"key":"3277_CR60","doi-asserted-by":"publisher","first-page":"e105","DOI":"10.3399\/bjgp14X677167","volume":"64","author":"J Chambers","year":"2014","unstructured":"Chambers J, Kabir S, Cajeat E. Detection of heart disease by open access echocardiography: a retrospective analysis of general practice referrals. Br J Gen Pract J R Coll Gen Pract. 2014;64:e105\u2013111. https:\/\/doi.org\/10.3399\/bjgp14X677167","journal-title":"Br J Gen Pract J R Coll Gen Pract"},{"key":"3277_CR61","doi-asserted-by":"publisher","first-page":"565","DOI":"10.1177\/0272989X06295361","volume":"26","author":"AJ Vickers","year":"2006","unstructured":"Vickers AJ, Elkin EB. Decision curve analysis: a novel method for evaluating prediction models. Med Decis Mak Int J Soc Med Decis Mak. 2006;26:565. https:\/\/doi.org\/10.1177\/0272989X06295361","journal-title":"Med Decis Mak Int J Soc Med Decis Mak"},{"key":"3277_CR62","doi-asserted-by":"publisher","first-page":"57","DOI":"10.1186\/s12911-025-02884-1","volume":"25","author":"JGO Marko","year":"2025","unstructured":"Marko JGO, Neagu CD, Anand PB. Examining inclusivity: the use of AI and diverse populations in health and social care: a systematic review. BMC Med Inf Decis Mak. 2025;25:57. https:\/\/doi.org\/10.1186\/s12911-025-02884-1","journal-title":"BMC Med Inf Decis Mak"},{"key":"3277_CR63","doi-asserted-by":"publisher","first-page":"2287","DOI":"10.1038\/s41598-024-52723-y","volume":"14","author":"N Ahmadi","year":"2024","unstructured":"Ahmadi N, Nguyen QV, Sedlmayr M, Wolfien M. A comparative patient-level prediction study in omop CDM: applicative potential and insights from synthetic data. Sci Rep. 2024;14:2287. https:\/\/doi.org\/10.1038\/s41598-024-52723-y","journal-title":"Sci Rep"},{"key":"3277_CR64","doi-asserted-by":"publisher","first-page":"180","DOI":"10.1186\/s12874-021-01370-2","volume":"21","author":"J Hardin","year":"2021","unstructured":"Hardin J, Reps JM. Evaluating the impact of covariate lookback times on performance of patient-level prediction models. BMC Med Res Methodol. 2021;21:180. https:\/\/doi.org\/10.1186\/s12874-021-01370-2","journal-title":"BMC Med Res Methodol"},{"key":"3277_CR65","doi-asserted-by":"publisher","first-page":"75","DOI":"10.1016\/j.healthpol.2021.12.004","volume":"126","author":"K Jacobs","year":"2022","unstructured":"Jacobs K, Roman E, Lambert J, Moke L, Scheys L, Kesteloot K, et al. Variability drivers of treatment costs in hospitals: a systematic review. Health Policy (new Y). 2022;126:75\u201386. https:\/\/doi.org\/10.1016\/j.healthpol.2021.12.004","journal-title":"Health Policy (new Y)"},{"key":"3277_CR66","doi-asserted-by":"publisher","first-page":"e0263718","DOI":"10.1371\/journal.pone.0263718","volume":"17","author":"A Nuako","year":"2022","unstructured":"Nuako A, Liu J, Pham G, Smock N, James A, Baker T, et al. Quantifying rural disparity in healthcare utilization in the United States: analysis of a large midwestern healthcare system. PLoS One. 2022;17:e0263718. https:\/\/doi.org\/10.1371\/journal.pone.0263718","journal-title":"PLoS One"},{"key":"3277_CR67","doi-asserted-by":"publisher","first-page":"7769","DOI":"10.22605\/RRH7769","volume":"23","author":"A Maganty","year":"2023","unstructured":"Maganty A, Byrnes ME, Hamm M, Wasilko R, Sabik LM, Davies BJ, et al. Barriers to rural health care from the provider perspective. Rural And Remote Health. 2023;23:7769. https:\/\/doi.org\/10.22605\/RRH7769","journal-title":"Rural And Remote Health"},{"key":"3277_CR68","doi-asserted-by":"publisher","first-page":"586","DOI":"10.1111\/j.1365-2796.2010.02274.x","volume":"268","author":"A Mackinnon","year":"2010","unstructured":"Mackinnon A. The use and reporting of multiple imputation in medical research \u2013 a review. J Intern Med. 2010;268:586\u201393. https:\/\/doi.org\/10.1111\/j.1365-2796.2010.02274.x","journal-title":"J Intern Med"},{"key":"3277_CR69","doi-asserted-by":"publisher","first-page":"134","DOI":"10.1186\/s12874-017-0414-5","volume":"17","author":"TR Sullivan","year":"2017","unstructured":"Sullivan TR, Lee KJ, Ryan P, Salter AB. Multiple imputation for handling missing outcome data when estimating the relative risk. BMC Med Res Methodol. 2017;17:134. https:\/\/doi.org\/10.1186\/s12874-017-0414-5","journal-title":"BMC Med Res Methodol"},{"key":"3277_CR70","doi-asserted-by":"publisher","first-page":"131","DOI":"10.1007\/s40258-023-00855-z","volume":"22","author":"B Shinkins","year":"2024","unstructured":"Shinkins B, Allen AJ, Karichu J, Garrison LP, Monz BU. Evidence synthesis and linkage for modelling the cost-effectiveness of diagnostic tests: preliminary good practice recommendations. Appl Health Econ Health Policy. 2024;22:131\u201344. https:\/\/doi.org\/10.1007\/s40258-023-00855-z","journal-title":"Appl Health Econ Health Policy"},{"key":"3277_CR71","doi-asserted-by":"publisher","first-page":"e14","DOI":"10.1017\/S0266462323000065","volume":"39","author":"F di Ruffano L","year":"2023","unstructured":"di Ruffano L F, Harris IM, Zhelev Z, Davenport C, Mallett S, Peters J, et al. Health technology assessment of diagnostic tests: a state of the art review of methods guidance from international organizations. Int J Technol Assess Health Care. 2023;39:e14. https:\/\/doi.org\/10.1017\/S0266462323000065","journal-title":"Int J Technol Assess Health Care"}],"container-title":["BMC Medical Informatics and Decision Making"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12911-025-03277-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s12911-025-03277-0","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12911-025-03277-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,12,30]],"date-time":"2025-12-30T01:55:47Z","timestamp":1767059747000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1186\/s12911-025-03277-0"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,11,26]]},"references-count":71,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2025,12]]}},"alternative-id":["3277"],"URL":"https:\/\/doi.org\/10.1186\/s12911-025-03277-0","relation":{"has-preprint":[{"id-type":"doi","id":"10.21203\/rs.3.rs-6623705\/v1","asserted-by":"object"}]},"ISSN":["1472-6947"],"issn-type":[{"value":"1472-6947","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,11,26]]},"assertion":[{"value":"8 May 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"7 November 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"26 November 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Not applicable.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"Not applicable.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Relevant guidelines and regulations"}},{"value":"The authors declare no competing interests.","order":5,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"452"}}