Real-world data: how they can help to improve quality of care
DOI:
https://doi.org/10.33393/grhta.2021.2286Keywords:
Data, Public health, Real WorldAbstract
The current COVID pandemic crisis made it even clearer that the solutions to several questions that public health must face require the access to good quality data. Several issues of the value and potential of health data and the current critical issues that hinder access are discussed in this paper. In particular, the paper (i) focuses on “real-world data” definition; (ii) proposes a review of the real-world data availability in our country; (iii) discusses its potential, with particular focus on the possibility of improving knowledge on the quality of care provided by the health system; (iv) emphasizes that the availability of data alone is not sufficient to increase our knowledge, underlining the need that innovative analysis methods (e.g., artificial intelligence techniques) must be framed in the paradigm of clinical research; and (v) addresses some ethical issues related to their use. The proposal is to realize an alliance between organizations interested in promoting research aimed at collecting scientifically solid evidence to support the clinical governance of public health.
Downloads
References
Liu K, Chen Y, Lin R, Han K. Clinical features of COVID-19 in elderly patients: A comparison with young and middle-aged patients. J Infect. 2020;80(6):e14-e18. https://doi.org/10.1016/j.jinf.2020.03.005 PMID:32171866 DOI: https://doi.org/10.1016/j.jinf.2020.03.005
Hewitt J, Carter B, Vilches-Moraga A, et al; COPE Study Collaborators. The effect of frailty on survival in patients with COVID-19 (COPE): a multicentre, European, observational cohort study. Lancet Public Health. 2020;5(8):e444-e451. https://doi.org/10.1016/S2468-2667(20)30146-8PMID:32619408
Clegg A, Young J, Iliffe S, Rikkert MO, Rockwood K. Frailty in elderly people. Lancet. 2013;381(9868):752-762. https://doi.org/10.1016/S0140-6736(12)62167-9 PMID:23395245 DOI: https://doi.org/10.1016/S0140-6736(12)62167-9
Rockwood K, Mitnitski A. Frailty in relation to the accumulation of deficits. J Gerontol A Biol Sci Med Sci. 2007;62(7):722-727. https://doi.org/10.1093/gerona/62.7.722 PMID:17634318 DOI: https://doi.org/10.1093/gerona/62.7.722
Hubbard RE, Peel NM, Samanta M, Gray LC, Mitnitski A, Rockwood K. Frailty status at admission to hospital predicts multiple adverse outcomes. Age Ageing. 2017;46(5):801-806. https://doi.org/10.1093/ageing/afx081 PMID:28531254 DOI: https://doi.org/10.1093/ageing/afx081
Rockwood K, Theou O. Using the clinical frailty scale in allocating scarce health care resource. Can Geriatr J. 2020;23(3):210-215. https://doi.org/10.5770/cgj.23.463 PMID:32904824 DOI: https://doi.org/10.5770/cgj.23.463
Einstein AJ, Shaw LJ, Hirschfeld C, et al; INCAPS COVID Investigators Group. International Impact of COVID-19 on the Diagnosis of Heart Disease. J Am Coll Cardiol. 2021;77(2):173-185. https://doi.org/10.1016/j.jacc.2020.10.054 PMID:33446311 DOI: https://doi.org/10.1016/j.jacc.2020.10.054
Wadhera RK, Shen C, Gondi S, Chen S, Kazi DS, Yeh RW. Cardiovascular Deaths During the COVID-19 Pandemic in the United States. J Am Coll Cardiol. 2021;77(2):159-169. https://doi.org/10.1016/j.jacc.2020.10.055 PMID:33446309 DOI: https://doi.org/10.1016/j.jacc.2020.10.055
Rosenbaum L. The Untold Toll - The Pandemic’s Effects on Patients without Covid-19. N Engl J Med. 2020;382(24):2368-2371. https://doi.org/10.1056/NEJMms2009984 PMID:32302076 DOI: https://doi.org/10.1056/NEJMms2009984
MaCroSCOPIO, Osservatorio sulla cronicità. COVID-19 e cronicità: gli impatti indiretti della pandemia. Available at: https://macroscopio.it/covid-e-cronicita/effetti-indiretti/. Last access: 22/07/2021
Higgins V, Sohaei D, Diamandis EP, Prassas I. COVID-19: from an acute to chronic disease? Potential long-term health consequences. Crit Rev Clin Lab Sci. 2021;58:297-310. https://doi.org/ 10.1080/10408363.2020.1860895 PMID:33347790 DOI: https://doi.org/10.1080/10408363.2020.1860895
Phillips S, Williams MA. Confronting Our Next National Health Disaster - Long-Haul Covid. N Engl J Med. 2021. Epub ahead of print. https://doi.org/10.1056/NEJMp2109285 PMID:34192429 DOI: https://doi.org/10.1056/NEJMp2109285
Lal A, Erondu NA, Heymann DL, Gitahi G, Yates R. Fragmented health systems in COVID-19: rectifying the misalignment between global health security and universal health coverage. Lancet. 2021;397(10268):61-67. https://doi.org/10.1016/S0140-6736(20)32228-5PMID:33275906 DOI: https://doi.org/10.1016/S0140-6736(20)32228-5
Ippolito G, Lauria FN, Locatelli F, et al. Lessons from the COVID-19 Pandemic-Unique Opportunities for Unifying, Revamping and Reshaping Epidemic Preparedness of Europe’s Public Health Systems. Int J Infect Dis. 2020;101:361-366. https://doi.org/10.1016/j.ijid.2020.10.094PMID:33152511 DOI: https://doi.org/10.1016/j.ijid.2020.10.094
Horton R. Offline: COVID-19 is not a pandemic. Lancet. 2020;396(10255):874. https://doi.org/10.1016/S0140-6736(20)32000-6 PMID:32979964 DOI: https://doi.org/10.1016/S0140-6736(20)32000-6
Bolislis WR, Fay M, Kühler TC. Use of Real-world Data for New Drug Applications and Line Extensions. Clin Ther. 2020;42(5):926-938. https://doi.org/10.1016/j.clinthera.2020.03.006PMID:32340916 DOI: https://doi.org/10.1016/j.clinthera.2020.03.006
Corrao G, Mancia G. Generating evidence from computerized healthcare utilization databases. Hypertension. 2015;65(3):490-498. https://doi.org/10.1161/HYPERTENSIONAHA.114.04858PMID:25624339 DOI: https://doi.org/10.1161/HYPERTENSIONAHA.114.04858
Trifirò G, Gini R, Barone-Adesi F, et al. The Role of European Healthcare Databases for Post-Marketing Drug Effectiveness, Safety and Value Evaluation: Where Does Italy Stand? Drug Saf. 2019;42(3):347-363. https://doi.org/10.1007/s40264-018-0732-5 PMID:30269245 DOI: https://doi.org/10.1007/s40264-018-0732-5
Schmidt H. Vaccine Rationing and the Urgency of Social Justice in the Covid-19 Response. Hastings Cent Rep. 2020;50(3):46-49. https://doi.org/10.1002/hast.1113 PMID:32468631 DOI: https://doi.org/10.1002/hast.1113
Mancia G, Rea F, Corrao G. RAAS Inhibitors and Risk of Covid-19. Reply. [Reply]. N Engl J Med. 2020;383(20):1993. PMID:33108106 DOI: https://doi.org/10.1056/NEJMc2030446
Bavishi C, Whelton PK, Mancia G, Corrao G, Messerli FH. Renin-angiotensin-system inhibitors and all-cause mortality in patients with COVID-19: a systematic review and meta-analysis of observational studies. J Hypertens. 2021;39(4):784-794; Epub ahead of print. https://doi.org/10.1097/HJH.0000000000002784 PMID:33560054 DOI: https://doi.org/10.1097/HJH.0000000000002784
Istituto Oncologico Veneto IRCCS. PDTA e linee guida. Online. https://www.ioveneto.it/prevenzione-e-cura/percorsi-diagnostici-terapeutici-assistenziali/
MaCroSCOPIO, Osservatorio sulla cronicità. Guide ai PDTA. Costruisci PDTA. Online https://macroscopio.it/guide-ai-pdta/costruisci-pdta/. Last access: 22/07/2021
MaCroSCOPIO, Osservatorio sulla cronicità. Guide ai PDTA. Valuta PDTA. Online https://macroscopio.it/guide-ai-pdta/valuta-pdta/. Last access: 22/07/2021
Wade D. Ethics of collecting and using healthcare data. BMJ. 2007;334(7608):1330-1331. https://doi.org/10.1136/bmj.39247.679329.80 PMID:17599978 DOI: https://doi.org/10.1136/bmj.39247.679329.80
Payne JL. Fishing expedition probability: The wtatistics of post hoc hypothesizing. Polity. 1974;7(1):130-138. www.jstor.org/stable/3234273. Accessed February 12, 2021. https://doi.org/10.2307/3234273 DOI: https://doi.org/10.2307/3234273
McLennan S, Lee MM, Fiske A, Celi LA. AI Ethics Is Not a Panacea. Am J Bioeth. 2020;20(11):20-22. https://doi.org/10.1080/15265161.2020.1819470 PMID:33103983 DOI: https://doi.org/10.1080/15265161.2020.1819470
Bærøe K, Jansen M, Kerasidou A. Machine Learning in Healthcare: Exceptional Technologies Require Exceptional Ethics. Am J Bioeth. 2020;20(11):48-51. https://doi.org/10.1080/15265161.2020.1820103 PMID:33103974 DOI: https://doi.org/10.1080/15265161.2020.1820103
Machine learning and artificial intelligence research for patient benefit: 20 critical questions on transparency, replicability, ethics, and effectiveness. BMJ. 2020;369:m1312. PMID:32238345 DOI: https://doi.org/10.1136/bmj.m1312
Mills JL. Data torturing. N Engl J Med. 1993;329(16):1196-1199. https://doi.org/10.1056/NEJM199310143291613 PMID:8166792 DOI: https://doi.org/10.1056/NEJM199310143291613
Garrison LP Jr, Neumann PJ, Erickson P, Marshall D, Mullins CD. Using real-world data for coverage and payment decisions: the ISPOR Real-World Data Task Force report. Value Health. 2007;10(5):326-335. https://doi.org/10.1111/j.1524-4733.2007.00186.x PMID:17888097 DOI: https://doi.org/10.1111/j.1524-4733.2007.00186.x
Downloads
Published
How to Cite
License
Copyright (c) 2021 The authors
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
Accepted 2021-07-30
Published 2021-09-21