Hospital Practices and Research

Current Issue

By Issue

By Author

By Subject

Author Index

Keyword Index

About Journal

Aims and Scope

Editorial Board

Editorial team

Publication Ethics

Indexing and Abstracting

Related Links

FAQ

Peer Review Process

News

The Future of Healthcare Facilities: How Technology and Medical Advances May Shape Hospitals of the Future

Document Type : Review Article

Authors

1 Iran University of Science and Technology, Tehran, Iran

2 Tehran East Branch, Payame Noor University, Tehran, Iran

10.15171/hpr.2019.01

Abstract

In this review article, we aim to depict how healthcare facilities may look in the near future from an architectural design point of view. For this purpose, we review newly introduced technology and medical advances in the field of healthcare, such as artificial intelligence (AI), robotic surgery, 3D printing, and information technology (IT), and suggest how those advances may affect the architectural design of future healthcare facilities. In future hospitals, less space will be required; there will be no need for waiting areas. Most care will be given far from the hospital. Every human might have a computer chip attached to his body, with all his medical data ready and monitored by AI. In the future, all processes may be done by robots and AI, from reception to detection (radiology, scans, etc.). Nearly all surgery will be done by robots, so the architectural design of operation departments will need to be changed accordingly. AI is faster and better in disease detection than man; thus, there will be no need for laboratories or detection departments as we know them now. 3D printers are able to print almost everything from medical equipment to parts of the human body; thus, space will be needed for scanning and 3D printing in future hospitals. 3D printers might change the pharmaceutical industries, and drugs will be produced for any human individually.

Keywords

References
  1. Xing L, Krupinski EA, Cai J. Artificial intelligence will soon change the landscape of medical physics research and practice. Med Phys. 2018;45(5):1791-1793. doi:10.1002/mp.12831.
  2. Patel VL, Shortliffe EH, Stefanelli M, et al. The coming of age of artificial intelligence in medicine. Artif Intell Med. 2009;46(1):5-17. doi:10.1016/j.artmed.2008.07.017.
  3. Bennett CC, Hauser K. Artificial intelligence framework for simulating clinical decision-making: a Markov decision process approach. Artif Intell Med. 2013;57(1):9-19. doi:10.1016/j.artmed.2012.12.003.
  4. Krittanawong C. The rise of artificial intelligence and the uncertain future for physicians. Eur J Intern Med. 2018;48:e13-e14. doi:10.1016/j.ejim.2017.06.017.
  5. Kononenko I. Machine learning for medical diagnosis: history, state of the art and perspective. Artif Intell Med. 2001;23(1):89- 109. doi:10.1016/S0933-3657(01)00077-X.
  6. Suzuki K. Pixel-based machine learning in medical imaging. Int J Biomed Imaging. 2012;2012:792079. doi:10.1155/2012/792079.
  7. Ibragimov B, Xing L. Segmentation of organs-at-risks in head and neck CT images using convolutional neural networks. Med Phys. 2017;44(2):547-557. doi:10.1002/mp.12045.
  8. Wang G, Kalra M, Orton CG. Machine learning will transform radiology significantly within the next 5 years. Med Phys. 2017;44(6):2041-2044. doi:10.1002/mp.12204.
  9. Mesko B. The role of artificial intelligence in precision medicine. Expert Rev Precis Med Drug Dev. 2017;2(5):239- 241. doi:10.1080/23808993.2017.1380516.
  10. Esteva A, Kuprel B, Novoa RA, et al. Dermatologist-level classification of skin cancer with deep neural networks. Nature. 2017;542(7639):115-118. doi:10.1038/nature21056.
  11. Rajpurkar P, Irvin J, Zhu K, et al. Chexnet: Radiologist-level pneumonia detection on chest x-rays with deep learning. arXiv; 2017.
  12. Mesanovic N, Grgic M, Huseinagic H, Males M, Skejic E, Smajlovic M. Automatic CT image segmentation of the lungs with region growing algorithm. In: 18th International Conference on Systems, Signals and Image Processing-IWSSIP; 2011.
  13. Nagar Y. Combining human and machine intelligence for making predictions. Massachusetts Institute of Technology (MIT); 2013.
  14. Collins FS, Varmus H. A new initiative on precision medicine. N Engl J Med. 2015;372(9):793-795. doi:10.1056/NEJMp1500523.
  15. Krupinski EA. The future of image perception in radiology: synergy between humans and computers. Acad Radiol. 2003;10(1):1-3. doi:10.1016/S1076-6332(03)80781-X.
  16. Hosny A, Parmar C, Quackenbush J, Schwartz LH, Aerts H. Artificial intelligence in radiology. Nat Rev Cancer. 2018;18(8):500-510. doi:10.1038/s41568-018-0016-5.
  17. Carlos RC, Kahn CE, Halabi S. Data science: big data, machine learning, and artificial intelligence. J Am Coll Radiol. 2018;15(3 Pt B):497-498. doi:10.1016/j.jacr.2018.01.029.
  18. Yu KH, Beam AL, Kohane IS. Artificial intelligence in healthcare. Nat Biomed Eng. 2018;2(10):719. doi:10.1038/s41551-018-0305-z.
  19. Houssami N, Lee CI, Buist DSM, Tao D. Artificial intelligence for breast cancer screening: Opportunity or hype? Breast. 2017;36:31-33. doi:10.1016/j.breast.2017.09.003.
  20. Ehteshami Bejnordi B, Veta M, Johannes van Diest P, et al. Diagnostic assessment of deep learning algorithms for detection of lymph node metastases in women with breast cancer. JAMA. 2017;318(22):2199-2210. doi:10.1001/jama.2017.14585.
  21. Majid A, Ali S, Iqbal M, Kausar N. Prediction of human breast and colon cancers from imbalanced data using nearest neighbor and support vector machines. Comput Methods Programs Biomed. 2014;113(3):792-808. doi:10.1016/j.cmpb.2014.01.001.
  22. Hippisley-Cox J, Coupland C, Vinogradova Y, et al. Predicting cardiovascular risk in England and Wales: prospective derivation and validation of QRISK2. BMJ. 2008;336(7659):1475-1482. doi:10.1136/bmj.39609.449676.25.
  23. Weng SF, Reps J, Kai J, Garibaldi JM, Qureshi N. Can machine-learning improve cardiovascular risk prediction using routine clinical data? PLoS One. 2017;12(4):e0174944. doi:10.1371/journal.pone.0174944.
  24. Das R, Turkoglu I, Sengur A. Effective diagnosis of heart disease through neural networks ensembles. Expert Syst Appl. 2009;36(4):7675-7680. doi:10.1016/j.eswa.2008.09.013.
  25. Muraru D, Badano LP, Piccoli G, et al. Validation of a novel automated border-detection algorithm for rapid and accurate quantitation of left ventricular volumes based on three-dimensional echocardiography. Eur J Echocardiogr. 2010;11(4):359-368. doi:10.1093/ejechocard/jep217.
  26. Smith KP, Kang AD, Kirby JE. Automated interpretation of blood culture Gram stains using a deep convolutional neural network. J Clin Microbiol. 2018;56(3). doi:10.1128/JCM.01521-17.
  27. Oguntimilehin A, Adetunmbi AO, Abiola OB. A Machine Learning Approach to Clinical Diagnosis of Typhoid Fever. International Journal of Computer and Information Technology. 2013;2(4):671-676.
  28. Knight W. Google Has Released an AI Tool That Makes Sense of Your Genome. https://www.technologyreview.com/s/609647/google-has-released-an-ai-tool-that-makes-sense-of-your-genome/. Accessed October 25, 2018.
  29. Bryant A, Wei B, Veronesi G, Cerfolio R. Robotic surgery: techniques and results for resection of lung cancer. In: Pass HI, Ball D, Scagliotti GV, eds. IASLC Thoracic Oncology. 2nd ed. Philadelphia: Content Repository Only!; 2018:283-288.e281. doi:10.1016/B978-0-323-52357-8.00028-7.
  30. Orekhov AL, Abah C, Simaan N. Snake-like robots for minimally invasive, single port, and intraluminal surgeries. The Encyclopedia of Medical Robotics; 2018:1. doi:10.1142/9789813232266_0008.
  31. Abbou CC, Hoznek A, Salomon L, et al. Laparoscopic radical prostatectomy with a remote controlled robot. J Urol. 2017;197(2s):S210-s212. doi:10.1016/j.juro.2016.10.107.
  32. van Mulken TJM, Schols RM, Qiu SS, et al. Robotic (super) microsurgery: Feasibility of a new master-slave platform in an in vivo animal model and future directions. J Surg Oncol. 2018;118(5):826-831. doi:10.1002/jso.25195.
  33. Dahroug B, Tamadazte B, Weber S, Tavernier L, Andreff N. Review on Otological Robotic Systems: Toward Microrobot- Assisted Cholesteatoma Surgery. IEEE Rev Biomed Eng. 2018;11:125-142. doi:10.1109/RBME.2018.2810605.
  34. von Scotti F, Kapsreiter M, Scherl C, Iro H, Bohr C. A 9-year analysis of transoral laser microsurgery (TLM) of head and neck cancer on their potential suitability for transoral robotic surgery (TORS) for estimation of future TORS-specific caseload. Eur Rev Med Pharmacol Sci. 2018;22(10):2949-2953. doi:10.26355/eurrev_201805_15049.
  35. van Mulken TJM, Boymans C, Schols RM, et al. Preclinical experience using a new robotic system created for microsurgery. Plast Reconstr Surg. 2018;142(5):1367-1376. doi:10.1097/PRS.0000000000004939.
  36. Li J, de Avila BE, Gao W, Zhang L, Wang J. Micro/nanorobots for biomedicine: Delivery, surgery, sensing, and detoxification. Sci Robot. 2017;2(4):eaam6431. doi:10.1126/scirobotics.aam6431.
  37. Mattos LS, Caldwell DG, Peretti G, Mora F, Guastini L, Cingolani R. Microsurgery robots: addressing the needs of high-precision surgical interventions. Swiss Med Wkly. 2016;146:w14375. doi:10.4414/smw.2016.14375.
  38. Friedrich DT, Scheithauer MO, Greve J, Hoffmann TK, Schuler PJ. Recent advances in robot-assisted head and neck surgery. Int J Med Robot. 2017;13(2). doi:10.1002/rcs.1744.
  39. Feuer GA, Lakhi N. Comparative study of survival outcomes following robotic-assisted laparoscopic versus abdominal surgery in the management of ovarian cancer. Gynecol Oncol. 2018;149 Suppl 1:231. doi:10.1016/j.ygyno.2018.04.522.
  40. Jaiprakash A, Roberts J, Crawford R. Robots in health care could lead to a doctorless hospital. The Conversation. 2016. https://theconversation.com/robots-in-health-care-could-lead-to-a-doctorless-hospital-54316. Accessed February 9, 2016.
  41. Alemzadeh H, Raman J, Leveson N, Kalbarczyk Z, Iyer RK. Adverse events in robotic surgery: a retrospective study of 14 years of FDA data. PLoS One. 2016;11(4):e0151470. doi:10.1371/journal.pone.0151470.
  42. Spector BL, Brooks NA, Strigenz ME, Brown JA. Bladder neck contracture following radical retropubic versus robotic-assisted laparoscopic prostatectomy. Curr Urol. 2017;10(3):145-149. doi:10.1159/000447169.
  43. Kaler KS, Vernez SL, Skarecky DW, Ahlering TE. Outcome Measures After Robot-Assisted Radical Prostatectomy. In: John H, Wiklund P, eds. Robotic Urology. Cham: Springer International Publishing; 2018:421-437. doi:10.1007/978-3-319-65864-3_37.
  44. Ventola CL. Medical applications for 3D printing: current and projected uses. P T. 2014;39(10):704-711.
  45. Schubert C, van Langeveld MC, Donoso LA. Innovations in 3D printing: a 3D overview from optics to organs. Br J Ophthalmol. 2014;98(2):159-161. doi:10.1136/bjophthalmol-2013-304446.
  46. Ursan ID, Chiu L, Pierce A. Three-dimensional drug printing: a structured review. J Am Pharm Assoc (2003). 2013;53(2):136- 144. doi:10.1331/JAPhA.2013.12217.
  47. Gu Y, Chen X, Lee JH, Monteiro DA, Wang H, Lee WY. Inkjet printed antibiotic- and calcium-eluting bioresorbable nanocomposite micropatterns for orthopedic implants. Acta Biomater. 2012;8(1):424-431. doi:10.1016/j.actbio.2011.08.006.
  48. Bonassar LJ, Rowley JA, Mooney DJ. Injection molding of living tissues. Google Patents; 2004.
  49. Rengier F, Mehndiratta A, von Tengg-Kobligk H, et al. 3D printing based on imaging data: review of medical applications. Int J Comput Assist Radiol Surg. 2010;5(4):335-341. doi:10.1007/s11548-010-0476-x.
  50. Reiffel AJ, Kafka C, Hernandez KA, et al. High-fidelity tissue engineering of patient-specific auricles for reconstruction of pediatric microtia and other auricular deformities. PLoS One. 2013;8(2):e56506. doi:10.1371/journal.pone.0056506.
  51. Jacobs S, Grunert R, Mohr FW, Falk V. 3D-Imaging of cardiac structures using 3D heart models for planning in heart surgery: a preliminary study. Interact Cardiovasc Thorac Surg. 2008;7(1):6-9. doi:10.1510/icvts.2007.156588.
  52. Hockaday LA, Kang KH, Colangelo NW, et al. Rapid 3D printing of anatomically accurate and mechanically heterogeneous aortic valve hydrogel scaffolds. Biofabrication. 2012;4(3):035005. doi:10.1088/1758-5082/4/3/035005.
  53. Mironov V, Boland T, Trusk T, Forgacs G, Markwald RR. Organ printing: computer-aided jet-based 3D tissue engineering. Trends Biotechnol. 2003;21(4):157-161. doi:10.1016/S0167-7799(03)00033-7.
  54. Mertz L. Dream it, design it, print it in 3-D: what can 3-D printing do for you? IEEE Pulse. 2013;4(6):15-21. doi:10.1109/MPUL.2013.2279616.
  55. Zhao Y, Yao R, Ouyang L, et al. Three-dimensional printing of Hela cells for cervical tumor model in vitro. Biofabrication. 2014;6(3):035001. doi:10.1088/1758-5082/6/3/035001.
  56. Bernhard JC, Isotani S, Matsugasumi T, et al. Personalized 3D printed model of kidney and tumor anatomy: a useful tool for patient education. World J Urol. 2016;34(3):337-345. doi:10.1007/s00345-015-1632-2.
  57. Duan B, Kapetanovic E, Hockaday LA, Butcher JT. Three-dimensional printed trileaflet valve conduits using biological hydrogels and human valve interstitial cells. Acta Biomater. 2014;10(5):1836-1846. doi:10.1016/j.actbio.2013.12.005.
  58. Duan B, Hockaday LA, Kang KH, Butcher JT. 3D bioprinting of heterogeneous aortic valve conduits with alginate/gelatin hydrogels. J Biomed Mater Res A. 2013;101(5):1255-1264. doi:10.1002/jbm.a.34420.
  59. Michalski MH, Ross JS. The shape of things to come: 3D printing in medicine. JAMA. 2014;312(21):2213-2214. doi:10.1001/jama.2014.9542.
  60. Bracaglia LG, Smith BT, Watson E, Arumugasaamy N, Mikos AG, Fisher JP. 3D printing for the design and fabrication of polymer-based gradient scaffolds. Acta Biomater. 2017;56:3- 13. doi:10.1016/j.actbio.2017.03.030.
  61. Zhang Q, Zhang F, Medarametla SP, Li H, Zhou C, Lin D. 3D printing of graphene aerogels. Small. 2016;12(13):1702-1708. doi:10.1002/smll.201503524.
  62. Nagarajan N, Dupret-Bories A, Karabulut E, Zorlutuna P, Vrana NE. Enabling personalized implant and controllable biosystem development through 3D printing. Biotechnol Adv. 2018;36(2):521-533. doi:10.1016/j.biotechadv.2018.02.004.
  63. Campbell PG, Weiss LE. Tissue engineering with the aid of inkjet printers. Expert Opin Biol Ther. 2007;7(8):1123-1127. doi:10.1517/14712598.7.8.1123.
  64. Bittner SM, Guo JL, Melchiorri A, Mikos AG. Three-dimensional Printing of Multilayered Tissue Engineering Scaffolds. Mater Today (Kidlington). 2018;21(8):861-874. doi:10.1016/j.mattod.2018.02.006.
  65. Peltola SM, Melchels FP, Grijpma DW, Kellomaki M. A review of rapid prototyping techniques for tissue engineering purposes. Ann Med. 2008;40(4):268-280. doi:10.1080/07853890701881788.
  66. Kim BS, Kwon YW, Kong JS, et al. 3D cell printing of in vitro stabilized skin model and in vivo pre-vascularized skin patch using tissue-specific extracellular matrix bioink: A step towards advanced skin tissue engineering. Biomaterials. 2018;168:38- 53. doi:10.1016/j.biomaterials.2018.03.040.
  67. Bradley D. 3D printing soft tissue scaffolds. Mater Today. 2018;21(4). doi:10.1016/j.mattod.2018.03.031.
  68. Chia HN, Wu BM. Recent advances in 3D printing of biomaterials. J Biol Eng. 2015;9:4. doi:10.1186/s13036-015-0001-4.
  69. Wang CC, Tejwani Motwani MR, Roach WJ, et al. Development of near zero-order release dosage forms using three-dimensional printing (3-DP) technology. Drug Dev Ind Pharm. 2006;32(3):367-376. doi:10.1080/03639040500519300.
  70. Rattanakit P, Moulton SE, Santiago KS, Liawruangrath S, Wallace GG. Extrusion printed polymer structures: a facile and versatile approach to tailored drug delivery platforms. Int J Pharm. 2012;422(1-2):254-263. doi:10.1016/j.ijpharm.2011.11.007.
  71. Sandler N, Maattanen A, Ihalainen P, et al. Inkjet printing of drug substances and use of porous substrates-towards individualized dosing. J Pharm Sci. 2011;100(8):3386-3395. doi:10.1002/jps.22526.
  72. Yu DG, Zhu LM, Branford-White CJ, Yang XL. Three-dimensional printing in pharmaceutics: promises and problems. J Pharm Sci. 2008;97(9):3666-3690. doi:10.1002/jps.21284.
  73. Preis M, Oblom H. 3D-printed drugs for children--are we ready yet? AAPS PharmSciTech. 2017;18(2):303-308. doi:10.1208/s12249-016-0704-y.
  74. Banks J. Adding value in additive manufacturing: researchers in the United Kingdom and Europe look to 3D printing for customization. IEEE Pulse. 2013;4(6):22-26. doi:10.1109/MPUL.2013.2279617.
  75. Grimson J, Grimson W, Hasselbring W. The SI challenge in health care. Commun ACM. 2000;43(6):48-55. doi:10.1145/336460.336474.
  76. Baldwin LP, Clarke M, Eldabi T, Jones RW. Telemedicine and its role in improving communication in healthcare. Logistics Information Management. 2002;15(4):309-319. doi:10.1108/09576050210436147.
  77. Aceto G, Persico V, Pescape A. The role of Information and Communication Technologies in healthcare: taxonomies, perspectives, and challenges. Journal of Network and Computer Applications. 2018;107:125-154. doi:10.1016/j.jnca.2018.02.008.
  78. Abel K, APN, Baldwin K, et al. Can Telemedicine Replace the First Post Op Visit for Knee Arthroscopy in Adolescents? Pediatrics. 2018;141(1):663. doi:10.1542/peds.141.1_MeetingAbstract.663.
  79. Dayal P, Hojman NM, Kissee JL, et al. Impact of telemedicine on severity of illness and outcomes among children transferred from referring emergency departments to a children’s hospital PICU. Pediatr Crit Care Med. 2016;17(6):516-521. doi:10.1097/PCC.0000000000000761.
  80. Brant M, Pouppirt N, Lioy J, Kim A, Chuo J. Telemedicine Visits Improve Transition to Home for Infants Requiring Complex Care. Pediatrics. 2018;141(1):173. doi:10.1542/peds.141.1_MeetingAbstract.173.
  81. Islam SMR, Kwak D, Kabir MH, Hossain M, Kwak K. The internet of things for health care: a comprehensive survey. IEEE Access. 2015;3:678-708. doi:10.1109/ACCESS.2015.2437951.
  82. Istepanian R, Laxminarayan S, Pattichis CS. M-health. Springer; 2006. doi:10.1007/b137697.
  83. Lisetti C, LeRouge C. Affective computing in tele-home health. In: Proceedings of the 37th Annual Hawaii International Conference on; 2004. doi:10.1109/HICSS.2004.1265373.
  84. Andersen TO, Bansler JP, Kensing F, et al. Aligning Concerns in Telecare: Three Concepts to Guide the Design of Patient- Centred E-Health. Comput Support Coop Work. 2018;27(3- 6):1181-1214. doi:10.1007/s10606-018-9309-1.
  85. Koch S. Home telehealth--current state and future trends. Int J Med Inform. 2006;75(8):565-576. doi:10.1016/j.ijmedinf.2005.09.002.
  86. Ruggiero C, Sacile R, Giacomini M. Home telecare. J Telemed Telecare. 1999;5(1):11-17. doi:10.1258/1357633991932333.
  87. Nakrem S, Solbjor M, Pettersen IN, Kleiven HH. Care relationships at stake? Home healthcare professionals’ experiences with digital medicine dispensers - a qualitative study. BMC Health Serv Res. 2018;18(1):26. doi:10.1186/s12913-018-2835-1.
  88. Serper M, Volk ML. Current and Future Applications of Telemedicine to Optimize the Delivery of Care in Chronic Liver Disease. Clin Gastroenterol Hepatol. 2018;16(2):157- 161.e158. doi:10.1016/j.cgh.2017.10.004.
  89. Telecare-Solutions. Telecare -Solutions. http://www.telecaresolutions.co.za/About.php. Accessed October 24, 2018.
  90. Koehler F, Koehler K, Deckwart O, et al. Telemedical Interventional Management in Heart Failure II (TIM-HF2), a randomised, controlled trial investigating the impact of telemedicine on unplanned cardiovascular hospitalisations and mortality in heart failure patients: study design and description of the intervention. Eur J Heart Fail. 2018;20(10):1485-1493. doi:10.1002/ejhf.1300.
  91. Blumenthal J, Wilkinson A, Chignell M. Physiotherapists’ and Physiotherapy Students’ Perspectives on the Use of Mobile or Wearable Technology in Their Practice. Physiother Can. 2018;70(3):251-261. doi:10.3138/ptc.2016-100.e.
  92. Elman JP. “Find Your Fit”: Wearable technology and the cultural politics of disability. New Media Soc. 2018;20(10):3760-3777. doi:10.1177/1461444818760312.
  93. Amft O. How Wearable Computing Is Shaping Digital Health. IEEE Pervasive Comput. 2018;17(1):92-98. doi:10.1109/MPRV.2018.011591067.
  94. Karuppiah M, Li X, Das AK, Kumari S, Liu Q. Introduction to the special section on Big data and IoT in e-healthcare. Computers & Electrical Engineering. 2018;65:261-264. doi:10.1016/j.compeleceng.2018.01.007.
  95. Jayaratne M, Nallaperuma D, De Silva D, et al. A data integration platform for patient-centered e-healthcare and clinical decision support. Future Gener Comput Syst. 2019;92:996-1008. doi:10.1016/j.future.2018.07.061.
  96. Sreedhar KC, Suresh Kumar N. An optimal cloud-based e-healthcare system using k-centroid MVS clustering scheme. J Intell Fuzzy Syst. 2018;34(3):1595-1607. doi:10.3233/JIFS-169454.
  97. Lymberis A. Progress in R&D on wearable and implantable biomedical sensors for better healthcare and medicine. In: 2005 3rd IEEE/EMBS Special Topic Conference on Microtechnology in Medicine and Biology. doi:10.1109/MMB.2005.1548453.
  98. Lymberis A, Gatzoulis L. Wearable health systems: from smart technologies to real applications. Conf Proc IEEE Eng Med Biol Soc. 2006;Suppl:6789-6792. doi:10.1109/IEMBS.2006.260948.
  99. Rizwan A, Zoha A, Zhang R, et al. A review on the role of nano-communication in future healthcare systems: A big data analytics perspective. IEEE Access. 2018;6:41903-41920. doi:10.1109/ACCESS.2018.2859340.
  100. Denny JC, Van Driest SL, Wei WQ, Roden DM. The Influence of Big (Clinical) Data and Genomics on Precision Medicine and Drug Development. Clin Pharmacol Ther. 2018;103(3):409- 418. doi:10.1002/cpt.951.
  101. Cai Y, Huang T. Accelerating precision medicine through genetic and genomic big data analysis. Biochim Biophys Acta Mol Basis Dis. 2018;1864(6 Pt B):2215-2217. doi:10.1016/j.bbadis.2018.03.012.
  102. Madhavan S, Subramaniam S, Brown TD, Chen JL. Art and Challenges of Precision Medicine: Interpreting and Integrating Genomic Data Into Clinical Practice. Am Soc Clin Oncol Educ Book. 2018(38):546-553. doi:10.1200/EDBK_200759.
  103. Manrai AK, Patel CJ, Ioannidis JPA. In the Era of Precision Medicine and Big Data, Who Is Normal? JAMA. 2018;319(19):1981-1982. doi:10.1001/jama.2018.2009.
Hospital Practices and Research
Volume 4, Issue 1 - Serial Number 13
February 2019
Pages 1-11
Files
History
  • Receive Date: 30 August 2018
  • Revise Date: 28 December 2018
  • Accept Date: 29 December 2018
Share
How to cite
Statistics