533
Views
0
CrossRef citations to date
0
Altmetric
Clinical Features - Review

The three horizons model applied to medical science

ORCID Icon, ORCID Icon, ORCID Icon, ORCID Icon, ORCID Icon, ORCID Icon & ORCID Icon show all
Pages 776-783 | Received 08 Mar 2022, Accepted 18 Aug 2022, Published online: 14 Sep 2022

References

  • Baghai M, Coley S, White D. The Alchemy of Growth.. 1st New York: Basic Books 1999. 271.
  • Blank S McKinsey’s three horizons model defined innovation for years. Here’s why it no longer applies. Harvard Business Review. 2019 [last accessed 2022 Jan 4]. Available at: https://hbr.org/2019/02/mckinseys-three-horizons-model-defined-innovation-for-years-heres-why-it-no-longer-applies.
  • Prasad V, Cifu A. Medical reversal: why we must raise the bar before adopting new technologies. Yale J Biol Med. 2011;84(4):471.
  • Fernandes TL, de Faria Rr, Gonzales MA, et al. Innovation in orthopaedics: part 2-How to translate ideas and research into clinical practice. Curr Rev Musculoskelet Med. 2022;15(2):150–155.
  • Beckmann JS, Lew D. Reconciling evidence-based medicine and precision medicine in the era of big data: challenges and opportunities. Genome Med. 2016;8(1):134.
  • Wynne R, Conway A, Davidson PM. Ensuring COVID-related innovation is sustained. J Adv Nurs. 2021;77(6):e4–e6.
  • Zelmer J. Beyond pilots: scaling and spreading innovation in healthcare. Healthcare Policy = Politiques de Sante. 2015;11(2):8–9.
  • Sisodiya SM. Precision medicine and therapies of the future. Epilepsia. 2021;62(Suppl S2):S90–S105.
  • Larisch L-M, Amer-Wåhlin I, Hidefjäll P. Understanding healthcare innovation systems: the Stockholm region case. Journal of Health Organization and Management. 2016;30(8):1573–1580.
  • Department of Clinical Epidemiology and Biostatistics, McMaster University. How to read clinical journals: I. Why to read them and how to start reading them critically. Can Med Assoc J. 1981;124(5):555–558.
  • Guyatt GH . Evidence-based medicine. ACP J Club. 1991;114(2):A–16
  • Isaacs D. Evidence-based medicine. J Paediatr Child Health. 2014;50(8):579–580
  • Lind J. A treatise of scurvy. in three parts. containing an enquiry into the nature, causes and cure, of that disease. together with a critical and chronological view of what has been published on the subject. Murray and Cochran: Edinburgh: Printed by Sands; 1753.
  • Chalmers I. Addressing uncertainties about the effects of treatments offered to NHS patients: whose responsibility? J R Soc Med. 2007;100(10):440–441.
  • Djulbegovic B, Guyatt GH. Progress in evidence-based medicine: a quarter century on. Lancet. 2017;390(10092):415–423.
  • Djulbegovic B, Guyatt GH, Ashcroft RE. Epistemologic inquiries in evidence-based medicine. Cancer Control. 2009;16(2):158–168.
  • Atkins D, Best D, Briss P, et al. Grading quality of evidence and strength of recommendations. BMJ. 2004;328:1490.
  • Miles A, Bentey P, Polychronis A, et al. Evidence-based medicine: why all the fuss? This is why. J Eval Clin Pract. 1997;3(2):83–86.
  • Chan AW. Bias, spin, and misreporting: time for full access to trial protocols and results. PLoS Med. 2008;5(11):e230.
  • Plant D, Barton A. Adding value to real-world data: the role of biomarkers. Rheumatology (Oxford). 2020;59(1):31–38.
  • Luce BR, Drummond M, Jönsson B, et al. EBM, HTA, and CER: clearing the confusion. Milbank Q. 2010;88(2):256–276.
  • Greenhalgh T. Integrating qualitative research into evidence-based practice. Endocrinol Metab Clin North Am. 2002;31(3):583–601.
  • Djulbegovic B, Guyatt GH. Evidence-based practice is not synonymous with delivery of uniform health care. JAMA. 2014;312(13):1293–1294.
  • Deana C. The COVID-19 pandemic: is our medicine still evidence-based? Ir J Med Sci. 2021;190(1):11–12.
  • Farquhar C, Marjoribanks J, Lethaby A, et al. High-dose chemotherapy and autologous bone marrow or stem cell transplantation versus conventional chemotherapy for women with early poor prognosis breast cancer. Cochrane Database Syst Rev. 2016 May 20;2016(5):CD003139.
  • Gilbert R, Salanti G, Harden M, et al. Infant sleeping position and the sudden infant death syndrome: systematic review of observational studies and historical review of recommendations from 1940 to 2002. Int J Epidemiol. 2005;34(4):874–887.
  • Early Breast Cancer Trialists’ Collaborative Group. Comparisons between different polychemotherapy regimens for early breast cancer: meta-analyses of long-term outcome among 100 000 women in 123 randomised trials. Lancet. 2012;379(9814):432–444.
  • Early Breast Cancer Trialists’ Collaborative Group (EBCTCG). Aromatase inhibitors versus tamoxifen in early breast cancer: patient-level meta-analysis of the randomised trials. Lancet. 2015;386(10001):1341–1352.
  • Siemieniuk RA, Meade MO, Alonso-Coello P, et al. Corticosteroid therapy for patients hospitalized with community-acquired pneumonia: a systematic review and meta-analysis. Ann Intern Med. 2015;163(7):519–528.
  • Rossouw J, Anderson G, Prentice R, et al. Risks and benefits of estrogen plus progestin in healthy postmenopausal women: principal results from the women’s health initiative randomized controlled trial. JAMA. 2002;288(3):321–333.
  • Makady A, de Boer A, Hillege H, et al. on behalf of getreal work package 1. what is real-world data? A review of definitions based on literature and stakeholder interviews. Value Health. 2017;20(7):858–865.
  • Koch-Henriksen N, Rasmussen S, Stenager E, et al. The Danish multiple sclerosis registry history, data collection and validity. Dan Med Bull. 2001;48(2):91–94.
  • Vetrugno L, Deana C, Maggiore SM. COVID-19 hurricane: recovering the worldwide health system with the RE.RE.RE. (REsponse-REstoration-REengineering) approach-Who will get there first? Healthcare (Basel). 2022;10(4):602.
  • Sherman RE, Anderson SA, Dal Pan GJ, et al. Real-world evidence - what is it and what can it tell us? N Engl J Med. 2016;375(23):2293–2297.
  • Mahajan R. Real world data: additional source for making clinical decisions. Int J Appl Basic Med Res. 2015;5(2):82.
  • Batrouni M, Comet D, Meunier JP. Real world studies, challenges, needs and trends from the industry. Value Health. 2014;17(7):A587–8.
  • Ahlbrandt J, Lablans M, Glocker K, et al. Modern information technology for cancer research: what’s in IT for me? An overview of technologies and approaches. Oncology. 2020;98(6):363–369.
  • Rumbold JM, Pierscionek BK. A critique of the regulation of data science in healthcare research in the European Union. BMC Med Ethics. 2017;18(1):27.
  • Hayens RB. Of studies, syntheses, synopses, summaries, and systems: the ‘5S’ evolution of information services for evidence-based healthcare decisions. Evid Based Med. 2006;11(6):162–164.
  • Bottomley A, Pe M, Sloan J, et al. Setting international standards in analyzing patient-reported outcomes and quality of life endpoints data (SISAQOL) consortium Analysing data from patient-reported outcome and quality of life endpoints for cancer clinical trials: a start in setting international standards. Lancet Oncol. 2016;17(11):e510–e514.
  • Bradshaw MJ, Farrow S, Motl RW, et al. Wearable biosensors to monitor disability in multiple sclerosis. Neurol Clin Pract. 2017;7(4):354–362.
  • Dickerson E, Davenport M, Syed F, et al. Effect of template reporting of brain MRIs for multiple sclerosis on report thoroughness and neurologist-rated quality: results of a prospective quality improvement project. J Am Coll Radiol. 2017;14(3):371–379.
  • Cui J, Stahl EA, Saevarsdottir S, et al. Genome-wide association study and gene expression analysis identifies CD84 as a predictor of response to etanercept therapy in rheumatoid arthritis. PLoS Genet. 2013;9(3):e1003394.
  • Sox HC, Lewis RJ. Pragmatic trials: practical answers to “real world” questions. JAMA. 2016;316(11):1205–1206.
  • Christian JB, Brouwer ES, Girman CJ, et al. Masking in pragmatic trials: who, what, and when to blind. Ther Innov Regul Sci. 2019. 10.1177/2168479019843129
  • GRADE Working Group: Morgan RL, Thayer KA, Santesso N, et al. A risk of bias instrument for non-randomized studies of exposures: a users‘ guide to its application in the context of GRADE. Environ Int. 2019;122:168–184.
  • Schwartz D, Lellouch J. Explanatory and pragmatic attitudes in therapeutical trials. J Clin Epidemiol. 2009;62(5):499–505.
  • Davies J, Martinec M, Martina R, et al. Retrospective indirect comparison of alectinib phase II data vs ceritinib real-world data in ALK+ NSCLC after progression on crizotinib. Ann Oncol. 2017;28(Suppl 10001):ii28–ii51. 10.
  • Golder S, Loke YK, Bland M. Meta-analyses of adverse effects data derived from randomised controlled trials as compared to observational studies: methodological overview. PLoS Med. 2011;8(5):e1001026.
  • Patsopoulos NA. A pragmatic view on pragmatic trials. Dialogues Clin Neurosci. 2011;13(2):363–369.
  • Ford I, Norrie J. Pragmatic trials. N Engl J Med. 2016;375(5):363–369.
  • Arts DG. Defining and improving data quality in medical registries: a literature review, case study, and generic framework. J Am Med Inform Assoc. 2002;9(6):162–164.
  • Eisenhauer EA. Real-world evidence in the treatment of ovarian cancer. Ann Oncol. 2017;28(Suppl. 8):viii61–viii65.
  • Pasello G, Pavan A, Attili I, et al. Real world data in the era of Immune Checkpoint Inhibitors (ICIs): increasing evidence and future applications in lung cancer. Cancer Treat Rev. 2020;87:102031.
  • Batra A, Rigo R, Sheka D, et al. Real-world evidence on adjuvant chemotherapy in older adults with stage II/III colon cancer. World J Gastrointest Oncol. 2020;12(6):604–618.
  • Feinberg BA, Gajra A, Zettler ME, et al. Use of real-world evidence to support FDA approval of oncology drugs. Value Health. 2020;23(10):1358–1365.
  • Caporali R, Zavaglia D. Real-world experience with tofacitinib for the treatment of rheumatoid arthritis. Clin Exp Rheumatol. 2019;37(3):485–495.
  • McGrath S, Ghersi D. Building towards precision medicine: empowering medical professionals for the next revolution. BMC Med Genomics. 2016;9(1):23.
  • König IR, Fuchs O, Hansen G, et al. What is precision medicine? Eur Respir J. 2017;50(4):1700391.
  • Delude CM. Deep phenotyping: the details of disease. Nature. 2015;527(7576):S14–S15.
  • Manrai AK, Ioannidis JP, Kohane IS. Clinical genomics: from pathogenicity claims to quantitative risk estimates. JAMA. 2016;315(12):1233–1234.
  • Barcia G, Chemaly N, Kuchenbuch M, et al. Epilepsy with migrating focal seizures: KCNT1 mutation hotspots and phenotype variability. Neurol Genet. 2019;5(6):e363.
  • Wray NR, Wijmenga C, Sullivan PF, et al. Common disease is more complex than implied by the core gene omnigenic model. Cell. 2018;173(7):1573–1580.
  • Jain SH, Powers BW, Hawkins JB, et al. The digital phenotype. Nat Biotechnol. 2015;33(5):462–463.
  • Hawgood S, Hook-Barnard IG, O’Brien TC, et al. Precision medicine: beyond the inflection point. Sci Transl Med. 2015;7(300):30017.
  • Roman-Belmonte JM, Corte-Rodriguez H, Rodriguez-Merchan EC. Artificial intelligence in musculoskeletal conditions. Front Biosci. 2021;26:1340–1348. Landmark.
  • McCorduck P. Machines who think. 2nd ed. Natick MA: Peters AK; 2004.
  • Suzuki K Overview of deep learning in medical imaging. Radiol Phys Technol. 2017;10(3):257–273. https://doi.org/10.1007/s12194-017-0406-5
  • Boissel J-P, Auffray C, Noble D, et al. Bridging systems medicine and patient needs. CPT Pharmacometrics Syst Pharmacol. 2015;4(3):e00026.
  • Ferté C, Trister AD, Huang E, et al. Impact of bioinformatic procedures in the development and translation of high-throughput molecular classifiers in oncology. Clin Cancer Res. 2013;19(16):4315–4325.
  • Graham JW. Missing data analysis: making it work in the real world. Ann Rev Psychol. 2009;60(1):549–576
  • Fowlkes EB, Gnanadesikan R, Kettenring JR. Variable selection in clustering. J Classif. 1988;5(2):205–228.
  • Richter AN, Khoshgoftaar TM. A review of statistical and machine learning methods for modeling cancer risk using structured clinical data. Artif Intell Med. 2018;90:1–14.
  • Kalafi EY, Nor NAM, Taib NA, et al. Machine Learning and Deep Learning Approaches in Breast Cancer Survival Prediction Using Clinical Data. Folia Biol (Praha). 2019;65(5–6):212–220.
  • Wallace E, Smith SM, Perera-Salazar R, et al. Framework for the impact analysis and implementation of clinical prediction rules (CPRs). BMC Med Inform Decis Mak. 2011;11(1):62.
  • Moons KGM, Altman DG, Reitsma JB, et al. Transparent reporting of a multivariable prediction model for Individual prognosis or diagnosis (TRIPOD): explanation and elaboration. Ann Intern Med. 2015;162(1):W1–73.
  • Raghupathi W, Raghupathi V. Big data analytics in healthcare: promise and potential. Health Information Science and Systems. 2014;2(1):3.
  • Agusti A, Bel E, Thomas M, et al. Treatable traits: toward precision medicine of chronic airway diseases. Eur Respir J. 2016;47(2):410–419.
  • Kasztura M, Richard A, Bempong N-E, et al. Cost-effectiveness of precision medicine: a scoping review. Int J Public Health. 2019;64(9):462–463.
  • Wong A, Otles E, Donnelly JP, et al. External validation of a widely implemented proprietary sepsis prediction model in hospitalized patients. JAMA Intern Med. 2021;181(8):1065–1070.
  • McCradden MD, Stephenson EA, Anderson JA. Clinical research underlies ethical integration of healthcare artificial intelligence. Nat Med. 2020;26(9):1325–1326.
  • Borysowski J, Ehni H-J, Górski A. Ethics codes and use of new and innovative drugs. Br J Clin Pharmacol. 2019;85(3):501–507.
  • Florez JC. Precision medicine in diabetes: is it time? Diabetes Care. 2016;39(7): 1085–1088.
  • Greenwalt I, Zaza N, Das S, et al. Precision medicine and targeted therapies in breast cancer. Surg Oncol Clin N Am. 2020;29(1):51–62.
  • McKinley TO, Lisboa FA, Horan AD, et al. Precision medicine applications to manage multiply injured patients with orthopaedic trauma. J Orthop Trauma. 2019;33(Suppl 3):S25–S29.
  • Striano P, Minassian BA. From genetic testing to precision medicine in epilepsy. Neurotherapeutics. 2020;17(2):609–615.

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.