Abstract
Fee For Service, also known as Volume Based Care (VBC) model of healthcare encourages service volume – more service more reward. This model of care results in unnecessary, inappropriate, and wasted medical services. In the US, Fraud, Waste, and Abuse (FWA) ranges between $760 billion to $935 billion, accounting for approximately 25% of total healthcare spending. In India, the waste caused by FWA is estimated to be as high as 35%. This is due to a lack of smart digital health, absence of AI models, and lack of preventive vigilance against inappropriate medical interventions. Inappropriate medical intervention costs valuable resources and causes patient harm. This paper proposes GREAT AI (Generative, Responsible, Explainable, Adaptive, and Trustworthy Artificial Intelligence) in Medical Appropriateness. We show how GREAT AI is used to offer appropriate medical services. Moreover, we show how GREAT AI can function in vigilance role to curb FWA. We present two GREAT AI models namely MAKG (Medical Appropriateness Knowledge Graph) and RAG-GPT (Retrieval Augmented Generation – Generative Pretrained Transformer). MAKG is used as an autonomous coarse-grained medical-inappropriateness vigilance model for payers and regulators. Whereas RAG-GPT is used as a fine-grained LLM, with human-in-the-loop for medical appropriateness and medical inappropriateness model where the actor human-in-the loop can be anybody like providers, patients, payers, regulators, funders, or researchers.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Sekhar M.S., Vyas N.: Defensive medicine: a bane to healthcare. Ann Med Health Sci Res. 2013;3(2):295–6 (2013). https://doi.org/10.4103/2141-9248.113688
Shrank, W.H., Rogstad, T.L., Parekh, N.: Waste in the US Health Care System: Estimated Costs and Potential for Savings. JAMA 322(15), 1501–1509 (2019). https://doi.org/10.1001/jama.2019.13978
Kamath, R., Brand, H.: A Critical Analysis of the World’s Largest Publicly Funded Health Insurance Program: India’s Ayushman Bharat. Int. J. Prev. Med. 14, 20 (2023). https://doi.org/10.4103/ijpvm.ijpvm_39_22
Detroit Area Doctor Sentenced to 45 Years in Prison for Providing Medically Unnecessary Chemotherapy to Patients: https://www.justice.gov/opa/pr/detroit-area-doctor-sentenced-45-years-prison-providing-medically-unnecessary-chemotherapy
Noncommunicable Disease: https://www.who.int/news-room/fact-sheets/detail/noncommunicable-diseases
Inpatient Prospective Payment System Rule. https://www.facs.org/advocacy/regulatory-issues/payment-rules/inpatient-prospective-payment-system-rule/
Fetter, R.B., Freeman, J.L.: Diagnosis related groups: Product line management within hospitals. Acad. Manag. Rev. 11(1), 41–54 (1986)
Ghosh, A.K., Ibrahim, S., Lee, J., Shapiro, M.F., Ancker, J.: Comparing Hospital Length of Stay Risk-Adjustment Models in US Value-Based Physician Payments. Qual. Manag. Health Care 32(1), 22–29 (2023). https://doi.org/10.1097/QMH.0000000000000363
Sackett, D.L., Rosenberg, W.M., Gray, J.A., Haynes, R.B., Richardson, W.S.: Evidence based medicine: what it is and what it isn’t. ClinOrthopRelat Res. 455, 3–5 (2007)
Brown T.B., Mann B., Ryder N., Subbiah M., Kaplan J., Dhariwal P., Neelakantan A., Shyam P., Sastry G., Askell A., Agarwal S., Herbert-Voss A., Krueger G., Henighan T., Child R., Ramesh A., Ziegler D.M., Wu J., Winter C., Hesse C., Chen M., Sigler E., Litwin M, Gray S., Chess B., Clark J., Berner C., McCandlish S., Radford A., Sutskever I., Amodei D.: Language Models are Few-Shot Learners. (2020). https://arxiv.org/abs/2005.14165
Kung, T.H., Cheatham, M., Medenilla, A., Sillos, C., De Leon, L., Elepaño, C., et al.: Performance of ChatGPT on USMLE: Potential for AI-assisted medical education using large language models. PLOS Digit Health. 2(2), e0000198 (2023). https://doi.org/10.1371/journal.pdig.0000198
Lewis P., Perez E., Piktus A., Petroni F., Karpukhin V., Goyal N., Küttler H., Lewis M., Yih W., Rocktäschel T., Riedel S., Kiela D.: Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks. https://arxiv.org/abs/2005.11401 (2020)
Hanlon, J.T., Schmader, K.E.: The Medication Appropriateness Index: A Clinimetric Measure. Psychother. Psychosom. 91(2), 78–83 (2022)
Santos-Eggimann, B., Paccaud, F., Blanc, T.: Medical appropriateness of hospital utilization: an overview of the Swiss experience. Int. J. Qual. Health Care 7(3), 227–232 (1995). https://doi.org/10.1093/intqhc/7.3.227
Lavis J.N., Anderson G.M.: Appropriateness in health care delivery: definitions, measurement, and policy implications. CMAJ. 154(3):321–8. PMID: 8564901 (1996)
Patient Safety in Healthcare, Forecast to 2022. https://store.frost.com/patient-safety-in-healthcare-forecast-to-2022.html
Rasmy, L., Xiang, Y., Xie, Z. et al.: Med-BERT: pretrained contextualized embeddings on large-scale structured electronic health records for disease prediction. npj Digit. Med. 4:86 (2021). https://doi.org/10.1038/s41746-021-00455-y
Iqbal, M.S., Abd-Alrazaq, A., Househ, M.: Artificial Intelligence Solutions to Detect Fraud in Healthcare Settings: A Scoping Review. Stud Health Technol Inform. 295, 20–23 (2022). https://doi.org/10.3233/SHTI220649
Sun, H., et al.: Medical Knowledge Graph to Enhance Fraud, Waste, and Abuse Detection on Claim Data: Model Development and Performance Evaluation. JMIR Med. Inform. 8(7), e17653 (2020). https://doi.org/10.2196/17653
Talukder, A.K., Selg, E., Fernandez, R., Raj, T.D.S., Waghmare, A.V., Haas, R.E.: Drugomics: Knowledge Graph & AI to Construct Physicians’ Brain Digital Twin to Prevent Drug Side-Effects and Patient Harm. In: Roy, P.P., Agarwal, A., Li, T., Krishna Reddy, P., Uday Kiran, R. (eds) Big Data Analytics. BDA 2022. Lecture Notes in Computer Science, vol 13773. Springer, Cham. ((2022). https://doi.org/10.1007/978-3-031-24094-2_10
Talukder, A.K., Schriml, L., Ghosh, A., Biswas, R., Chakrabarti, P., Haas, R.E.: Diseasomics: Actionable machine interpretable disease knowledge at the point-of-care. PLOS Digit Health. 1(10), e0000128 (2022). https://doi.org/10.1371/journal.pdig.0000128
Talukder, A.K., Selg, E., Haas, R.E.: Physicians’ Brain Digital Twin: Holistic Clinical & Biomedical Knowledge Graphs for Patient Safety and Value-Based Care to Prevent the Post-pandemic Healthcare Ecosystem Crisis. In: Villazón-Terrazas, B., Ortiz-Rodriguez, F., Tiwari, S., Sicilia, MA., Martín-Moncunill, D. (eds) Knowledge Graphs and Semantic Web. KGSWC 2022. Communications in Computer and Information Science, vol 1686. Springer, Cham (2022). https://doi.org/10.1007/978-3-031-21422-6_3
Classen, D.C., Longhurst, C., Thomas, E.J.: Bending the patient safety curve: how much can AI help? npj Digit. Med. 6, 2 (2023). https://doi.org/10.1038/s41746-022-00731-5
Garnelo, M., Shanahan, M.: Reconciling deep learning with symbolic artificial intelligence: representing objects and relations. Curr. Opin. Behav. Sci.Behav. Sci. 29, 17–23 (2019). https://doi.org/10.1016/j.cobeha.2018.12.010
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Datta, V.D., Ganesh, S., Haas, R.E., Talukder, A.K. (2023). GREAT AI in Medical Appropriateness and Value-Based-Care. In: Goyal, V., Kumar, N., Bhowmick, S.S., Goyal, P., Goyal, N., Kumar, D. (eds) Big Data and Artificial Intelligence. BDA 2023. Lecture Notes in Computer Science, vol 14418. Springer, Cham. https://doi.org/10.1007/978-3-031-49601-1_2
Download citation
DOI: https://doi.org/10.1007/978-3-031-49601-1_2
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-49600-4
Online ISBN: 978-3-031-49601-1
eBook Packages: Computer ScienceComputer Science (R0)