Application of P4 (Predictive, Preventive, Personalized, Participatory) Approach to Occupational Medicine
Main Article Content
Keywords
P4 medicine, Occupational medicine
Abstract
In recent years there has been a growth in the role of prevention in controlling the disease burden. Increasing efforts have been conveyed in the screening implementation and public health policies, and the spreading knowledge on risk factors reflects on major attention to health checks. Despite this, lifestyle changes are difficult to be adopted and the adherence to current public health services like screening and vaccinations remains suboptimal. Additionally, the prevalence and outcome of different chronic diseases and cancers is burdened by social disparities. P4 [predictive, preventive, personalized, participatory] medicine is the conceptualization of a new health care model, based on multidimensional data and machine-learning algorithms in order to develop public health intervention and monitoring the health status of the population with focus on wellbeing and healthy ageing. Each of the characteristics of P4 medicine is relevant to occupational medicine, and indeed the P4 approach appears to be particularly relevant to this discipline. In this review, we discuss the potential applications of P4 to occupational medicine, showing examples of its introduction on workplaces and hypothesizing its further implementation at the occupational level.
References
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42. Zou J, Wang E. Cancer Biomarker Discovery for Precision Medicine: New Progress. Curr Med Chem. 2019;26:7655-71.
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53. Balk-Møller NC, Poulsen SK, Larsen TM. Effect of a Nine-Month Web- and App-Based Workplace Intervention to Promote Healthy Lifestyle and Weight Loss for Employees in the Social Welfare and Health Care Sector: A Randomized Controlled Trial. J Med Internet Res. 2017;19:e108.
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55. Wang Z, Wang X, Shen Y, Li S, Chen Z, Zheng C, et al; China Hypertension Survey Group: The Standardized Management of Hypertensive Employees Program. Effect of a Workplace-Based Multicomponent Intervention on Hypertension Control: A Randomized Clinical Trial. JAMA Cardiol. 2020;5:567-75.
56. Dashti HS, Hivert MF, Levy DE, McCurley JL, Saxena R, Thorndike AN. Polygenic risk score for obesity and the quality, quantity, and timing of workplace food purchases: A secondary analysis from the ChooseWell 365 randomized trial. PLoS Med. 2020;17:e1003219.
57. Baghdadi A, Megahed FM, Esfahani ET, Cavuoto LA. A machine learning approach to detect changes in gait parameters following a fatiguing occupational task. Ergonomics. 2018;61:1116-29.
58. Na KS, Kim E. A Machine Learning-Based Predictive Model of Return to Work After Sick Leave. J Occup Environ Med. 2019;61:e191-9.
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68. Yanes T, Young MA, Meiser B, James PA. Clinical applications of polygenic breast cancer risk: a critical review and perspectives of an emerging field. Breast Cancer Res. 2020;22:21.
69. Jia G, Wen W, Massion PP, Shu XO, Zheng W. Incorporating both genetic and tobacco smoking data to identify high-risk smokers for lung cancer screening. Carcinogenesis. 2021;42:874-9.
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2. Schüssler-Fiorenza Rose SM, Contrepois K, Moneghetti KJ, Zhou W, Mishra T, Mataraso S, et al. A longitudinal big data approach for precision health. Nat Med. 2019;25:792-804.
3. Bycroft C, Freeman C, Petkova D, et al. The UK Biobank resource with deep phenotyping and genomic data. Nature. 2018;562:203-9.
4. Alam MJ, Rahman MF. Herd Immunity: A Brief Review. Mymensingh Med J. 2016;25:392-5.
5. Merle T, Jeannot E. Surveillance of vaccination coverage in 5-6- and 13-14-years-old schoolchildren in Geneva. Arch Pediatr. 2020;27:292-6.
6. Rizzo C, Rezza G, Ricciardi W. Strategies in recommending influenza vaccination in Europe and US. Hum Vaccin Immunother. 2018;14:693-8.
7. Walling EB, Benzoni N, Dornfeld J, Bhandari R, Sisk BA, Garbutt J, et al. Interventions to Improve HPV Vaccine Uptake: A Systematic Review. Pediatrics. 2016;138:e20153863.
8. Gostin, L.O.; Salmon, D.A. The Dual Epidemics of COVID-19 and Influenza: Vaccine Acceptance, Coverage, and Mandates. JAMA 2020;324:3356.
9. Doherty TM, Di Pasquale A, Michel JP, Del Giudice G. Precision Medicine and Vaccination of Older Adults: From Reactive to Proactive (A Mini-Review). Gerontology. 2020;66:238-48.
10. Zimmermann P, Curtis N. Why is COVID-19 less severe in children? A review of the proposed mechanisms underlying the age-related difference in severity of SARS-CoV-2 infections. Arch Dis Child. 2020:2020:320338.
11. Wooldridge M. Risk modelling for vaccination: a risk assessment perspective. Dev Biol 2007;130:87-97.
12. Forni G, Mantovani A; COVID-19 Commission of Accademia Nazionale dei Lincei, Rome. COVID-19 vaccines: where we stand and challenges ahead. Cell Death Differ. 2021;28:626-39.
13. Stokel-Walker, C. Covid-19: The countries that have mandatory vaccination for health workers. BMJ 2021;373:n1645.
14. Chen J, See KC. Artificial Intelligence for COVID-19: Rapid Review. J Med Internet Res. 2020;22:e21476.
15. Wong CK, Ho DTY, Tam AR, Zhou M, Lau YM, Tang MOY, et al. Artificial intelligence mobile health platform for early detection of COVID-19 in quarantine subjects using a wearable biosensor: protocol for a randomised controlled trial. BMJ Open. 2020;10:e038555.
16. Sarker S, Jamal L, Ahmed SF, Irtisam N. Robotics and artificial intelligence in healthcare during COVID-19 pandemic: A systematic review. Rob Auton Syst. 2021146:103902.
17. Whitelaw S, Mamas MA, Topol E, Van Spall HGC. Applications of digital technology in COVID-19 pandemic planning and response. Lancet Digit Health. 2020;2:e435-0.
18. Alami H, Rivard L, Lehoux P, Hoffman SJ, Cadeddu SBM, Savoldelli M, et al. Artificial intelligence in health care: laying the Foundation for Responsible, sustainable, and inclusive innovation in low- and middle-income countries. Global Health. 2020;16:52.
19. Cheng C, Beauchamp A, Elsworth GR, Osborne RH. Applying the Electronic Health Literacy Lens: Systematic Review of Electronic Health Interventions Targeted at Socially Disadvantaged Groups. J Med Internet Res. 2020;22:e18476.
20. https://www.nytimes.com/interactive/2021/us/covid-cases.html (Accessed November 18th, 2021).
21. Schwalbe N, Wahl B. Artificial intelligence and the future of global health. Lancet. 2020;395:1579-86.
22. Brunskill E, Lesh N. Routing for rural health: optimizing community health worker visit schedules. Conference: Artificial Intelligence for Development, Papers from the 2010 AAAI Spring Symposium, Technical Report SS-10-01, Stanford, California, USA, March 22-24, 2010. https://www.cs.cmu.edu/~ebrun/brunskilllesh.pdf (Accessed November 18th, 2021).
23. Boffetta P, Farioli A, Rizzello E. Application of epidemiological findings to individuals. Med Lav. 2020;111:10-21.
24. Hassan C, Wallace MB, Sharma P, Maselli R, Craviotto V, Spadaccini M, et al. A. New artificial intelligence system: first validation study versus experienced endoscopists for colorectal polyp detection. Gut. 2020;69:799-800.
25. Wang KW, Dong M. Potential applications of artificial intelligence in colorectal polyps and cancer: Recent advances and prospects. World J Gastroenterol. 2020;26:5090-100.
26. Vinsard DG, Mori Y, Misawa M, Kudo SE, Rastogi A, Bagci U, et al. Quality assurance of computer-aided detection and diagnosis in colonoscopy. Gastrointest Endosc. 2019;90:55-63.
27. Grzybowski A, Brona P, Lim G, Ruamviboonsuk P, Tan GSW, Abramoff M, et al. Artificial intelligence for diabetic retinopathy screening: a review. Eye (Lond). 2020;;34:451-60.
28. Attia ZI, Noseworthy PA, Lopez-Jimenez F, Asirvatham SJ, Deshmukh AJ, Gersh BJ, et al. An artificial intelligence-enabled ECG algorithm for the identification of patients with atrial fibrillation during sinus rhythm: a retrospective analysis of outcome prediction. Lancet. 2019;394:861-7.
29. Bibault JE, Xing L. Screening for chronic obstructive pulmonary disease with artificial intelligence. Lancet Digit Health. 2020;2:e216-7.
30. D'Antoni F, Russo F, Ambrosio L, Vollero L, Vadalà G, Merone M, et al. Artificial Intelligence and Computer Vision in Low Back Pain: A Systematic Review. Int J Environ Res Public Health. 2021;18:10909.
31. Zhou ZR, Wang WW, Li Y, Jin KR, Wang XY, Wang ZW, et al. In-depth mining of clinical data: the construction of clinical prediction model with R. Ann Transl Med. 2019;7:796.
32. Bibault JE. Real-life clinical data mining: generating hypotheses for evidence-based medicine. Ann Transl Med. 2020;8:69.
33. Garratt KN, Schneider MA. Thinking Machines and Risk Assessment: On the Path to Precision Medicine. J Am Heart Assoc. 2019;8:e011969.
34. Castaldi PJ, Boueiz A, Yun J, Estepar RSJ, Ross JC, Washko G, et al. Machine Learning Characterization of COPD Subtypes: Insights From the COPDGene Study. Chest. 2020;157:1147-57.
35. Miller DD, Brown EW. Artificial Intelligence in Medical Practice: The Question to the Answer? Am J Med. 2018;131:129-33.
36. Jaeger S, Juarez-Espinosa OH, Candemir S. Detecting drug-resistant tuberculosis in chest radiographs. Int J CARS. 2018;13:1915–25.
37. Phakhounthong K, Chaovalit P, Jittamala P. Predicting the severity of dengue fever in children on admission based on clinical features and laboratory indicators: application of classification tree analysis. BMC Pediatr. 2018;18:109.
38. Jiang D, Hao M, Ding F, Fu J, Li M. Mapping the transmission risk of Zika virus using machine learning models. Acta Trop. 2018;185:391–9.
39. Moyo S, Doan TN, Yun JA, Tshuma N. Application of machine learning models in predicting length of stay among healthcare workers in underserved communities in South Africa. Hum Resour Health. 2018;16:68.
40. Barbour DL, Howard RT, Song XD, Metzger N, Sukesan KA, DiLorenzo JC, et al. Online Machine Learning Audiometry. Ear Hear. 2019;40:918-26.
41 Bollati V, Ferrari L, Leso V, Iavicoli I. Personalised Medicine: implication and perspectives in the field of occupational health. Med Lav. 2020;111:425-44.
42. Zou J, Wang E. Cancer Biomarker Discovery for Precision Medicine: New Progress. Curr Med Chem. 2019;26:7655-71.
43. Reif J, Chan D, Jones D, Payne L, Molitor D. Effects of a Workplace Wellness Program on Employee Health, Health Beliefs, and Medical Use: A Randomized Clinical Trial. JAMA Intern Med. 2020;180:952-60.
44. Jiménez-Mérida MR, Romero-Saldaña M, Molina-Luque R, Molina-Recio G, Meneses-Monroy A, De Diego-Cordero R, et al. Women-centred workplace health promotion interventions: a systematic review. Int Nurs Rev. 2021;68:90-8.
45. Poscia A, Moscato U, La Milia DI, Milovanovic S, Stojanovic J, Borghini A, et al. Workplace health promotion for older workers: a systematic literature review. BMC Health Serv Res. 2016;16 Suppl 5:329.
46. Orlando LA, Wu RR, Myers RA, Neuner J, McCarty C, Haller IV, et al. At the intersection of precision medicine and population health: an implementation-effectiveness study of family health history based systematic r sk assessment in primary care. BMC Health Serv Res. 2020;20:1015.
47. Yerrakalva D, Yerrakalva D, Hajna S, Griffin S. Effects of Mobile Health App Interventions on Sedentary Time, Physical Activity, and Fitness in Older Adults: Systematic Review and Meta-Analysis. J Med Internet Res. 2019;21:e14343.
48. Young KP, Kolcz DL, O'Sullivan DM, Ferrand J, Fried J, Robinson K. Health Care Workers' Mental Health and Quality of Life During COVID-19: Results From a Mid-Pandemic, National Survey. Psychiatr Serv. 2021;72:122-8.
49. Gray P, Senabe S, Naicker N, Kgalamono S, Yassi A, Spiegel JM. Workplace-Based Organizational Interventions Promoting Mental Health and Happiness among Healthcare Workers: A Realist Review. Int J Environ Res Public Health. 2019;16:4396.
50. Jones DE, Weaver MT, Friedmann E. Promoting heart health in women: a workplace intervention to improve knowledge and perceptions of susceptibility to heart disease. AAOHN J. 2007;55:271-6.
51. Thorndike AN, McCurley JL, Gelsomin ED, Anderson E, Chang Y, Porneala B, et al. Automated Behavioral Workplace Intervention to Prevent Weight Gain and Improve Diet: The ChooseWell 365 Randomized Clinical Trial. JAMA Netw Open. 2021;4:e2112528.
52. Heinlen C, Hovick SR, Brock GN, Klamer BG, Toland AE, Senter L. Exploring genetic counselors' perceptions of usefulness and intentions to use refined risk models in clinical care based on the Technology Acceptance Model (TAM). J Genet Couns. 2019;28:664-72.
53. Balk-Møller NC, Poulsen SK, Larsen TM. Effect of a Nine-Month Web- and App-Based Workplace Intervention to Promote Healthy Lifestyle and Weight Loss for Employees in the Social Welfare and Health Care Sector: A Randomized Controlled Trial. J Med Internet Res. 2017;19:e108.
54. Salinardi TC, Batra P, Roberts SB, Urban LE, Robinson LM, Pittas AG, et al. Lifestyle intervention reduces body weight and improves cardiometabolic risk factors in worksites. Am J Clin Nutr. 2013;97:667-76.
55. Wang Z, Wang X, Shen Y, Li S, Chen Z, Zheng C, et al; China Hypertension Survey Group: The Standardized Management of Hypertensive Employees Program. Effect of a Workplace-Based Multicomponent Intervention on Hypertension Control: A Randomized Clinical Trial. JAMA Cardiol. 2020;5:567-75.
56. Dashti HS, Hivert MF, Levy DE, McCurley JL, Saxena R, Thorndike AN. Polygenic risk score for obesity and the quality, quantity, and timing of workplace food purchases: A secondary analysis from the ChooseWell 365 randomized trial. PLoS Med. 2020;17:e1003219.
57. Baghdadi A, Megahed FM, Esfahani ET, Cavuoto LA. A machine learning approach to detect changes in gait parameters following a fatiguing occupational task. Ergonomics. 2018;61:1116-29.
58. Na KS, Kim E. A Machine Learning-Based Predictive Model of Return to Work After Sick Leave. J Occup Environ Med. 2019;61:e191-9.
59. Goldstein P, Ashar Y, Tesarz J, Kazgan M, Cetin B, Wager TD. Emerging Clinical Technology: Application of Machine Learning to Chronic Pain Assessments Based on Emotional Body Maps. Neurotherapeutics. 2020;17:774-83.]
60. IsHak WW, Wen RY, Naghdechi L, Vanle B, Dang J, Knosp M, et al. Pain and Depression: A Systematic Review. Harv Rev Psychiatry. 2018;26:352-63.
61. Cabitza F, Rasoini R, Gensini GF. Unintended Consequences of Machine Learning in Medicine. JAMA. 2017;318:517-18.
62. Ingram C, Downey V, Roe M, Chen Y, Archibald M, Kallas KA, et al. COVID-19 Prevention and Control Measures in Workplace Settings: A Rapid Review and Meta-Analysis. Int J Environ Res Public Health. 2021;18:7847.
63. Soleimanpour S, Yaghoubi A. COVID-19 vaccine: where are we now and where should we go? Expert Rev Vaccines. 2021;20:23-44.
64. Doustmohammadi S, Cherry JD. The sociology of the antivaccine movement. Emerg Top Life Sci. 2020;4:241-5.
65. Sharrer GT. Personalized Medicine: Ethical Aspects. Methods Mol Biol. 2017;1606:37-50.
66. Rebbeck TR, Burns-White K, Chan AT, Emmons K, Freedman M, Hunter DJ, et al. Precision Prevention and Early Detection of Cancer: Fundamental Principles. Cancer Discov. 2018;8:803-11.
67. Benke K, Benke G. Artificial Intelligence and Big Data in Public Health. Int J Environ Res Public Health. 2018;15:2796.
68. Yanes T, Young MA, Meiser B, James PA. Clinical applications of polygenic breast cancer risk: a critical review and perspectives of an emerging field. Breast Cancer Res. 2020;22:21.
69. Jia G, Wen W, Massion PP, Shu XO, Zheng W. Incorporating both genetic and tobacco smoking data to identify high-risk smokers for lung cancer screening. Carcinogenesis. 2021;42:874-9.
70. Blechter B, Wong JYY, Agnes Hsiung C, Hosgood HD, Yin Z, Shu XO, et al. Sub-multiplicative interaction between polygenic risk score and household coal use in relation to lung adenocarcinoma among never-smoking women in Asia. Environ Int. 2021;147:105975.
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Boffetta P, Collatuzzo G. Application of P4 (Predictive, Preventive, Personalized, Participatory) Approach to Occupational Medicine. Med Lav [Internet]. 2022 Feb. 22 [cited 2024 Jul. 18];113(1):e2022009. Available from: https://mattioli1885journals.com/index.php/lamedicinadellavoro/article/view/12622
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How to Cite
1.
Boffetta P, Collatuzzo G. Application of P4 (Predictive, Preventive, Personalized, Participatory) Approach to Occupational Medicine. Med Lav [Internet]. 2022 Feb. 22 [cited 2024 Jul. 18];113(1):e2022009. Available from: https://mattioli1885journals.com/index.php/lamedicinadellavoro/article/view/12622
