Hostname: page-component-cd9895bd7-gxg78 Total loading time: 0 Render date: 2024-12-25T18:28:53.021Z Has data issue: false hasContentIssue false
  • English
  • Français

MEDICAL DEVICES EARLY ASSESSMENT METHODS: SYSTEMATIC LITERATURE REVIEW

Part of: Special Collection - Methods

Published online by Cambridge University Press:  07 May 2014

Katarzyna Markiewicz ,
Janine A. van Til  and
Maarten J. IJzerman
Katarzyna Markiewicz
Affiliation:
University of Twente, Health Technology and Services Research, MIRA Institute for Biomedical Technology and Technical Medicine
Janine A. van Til
Affiliation:
University of Twente, Health Technology and Services Research, MIRA Institute for Biomedical Technology and Technical Medicine
Maarten J. IJzerman
Affiliation:
University of Twente, Health Technology and Services Research, MIRA Institute for Biomedical Technology and Technical Medicine
Get access Rights & Permissions [Opens in a new window]

Abstract

Objectives: The aim of this study was to get an overview of current theory and practice in early assessments of medical devices, and to identify aims and uses of early assessment methods used in practice.

Methods: A systematic literature review was conducted in September 2013, using computerized databases (PubMed, Science Direct, and Scopus), and references list search. Selected articles were categorized based on their type, objective, and main target audience. The methods used in the application studies were extracted and mapped throughout the early stages of development and for their particular aims.

Results: Of 1,961 articles identified, eighty-three studies passed the inclusion criteria, and thirty were included by searching reference lists. There were thirty-one theoretical papers, and eighty-two application papers included. Most studies investigated potential applications/possible improvement of medical devices, developed early assessment framework or included stakeholder perspective in early development stages. Among multiple qualitative and quantitative methods identified, only few were used more than once. The methods aim to inform strategic considerations (e.g., literature review), economic evaluation (e.g., cost-effectiveness analysis), and clinical effectiveness (e.g., clinical trials). Medical devices were often in the prototype product development stage, and the results were usually aimed at informing manufacturers.

Conclusions: This study showed converging aims yet widely diverging methods for early assessment during medical device development. For early assessment to become an integral part of activities in the development of medical devices, methods need to be clarified and standardized, and the aims and value of assessment itself must be demonstrated to the main stakeholders for assuring effective and efficient medical device development.

Keywords

Health technology assessmentMedical devicesHealthcare economics and organizationsCost-effectivenessTranslational research
Type
Methods
Information
International Journal of Technology Assessment in Health Care , Volume 30 , Issue 2 , April 2014 , pp. 137 - 146
Copyright
Copyright © Cambridge University Press 2014 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

REFERENCES

1. Bartelmes, M, Neumann, U, Lühmann, D, Schönermark, MP, Hagen, A. Methoden zur frühen entwicklungsbegleitenden Bewertung innovativer medizinischer Technologien. GMS Health Technol Assess. 2009;5:15.Google Scholar
2. Lim, ME, O'Reilly, D, Tarride, JE, et al. Health technology assessment for radiologists: Basic principles and evaluation framework. J Am Coll Radiol. 2009;6:299–306.CrossRefGoogle ScholarPubMed
3. Vallejo-Torres, L, Steuten, LMG, Parkinson, B, Girling, AJ, Buxton, MJ. Integrating health economics modeling in the product development cycle of medical devices: A Bayesian approach. Int J Technol Assess Health Care. 2008;24:459–464.CrossRefGoogle ScholarPubMed
4. Goodman, CS, Ahn, R. Methodological approaches of health technology assessment. Int J Med Inform. 1999;56:97–105.CrossRefGoogle ScholarPubMed
5. Ferrusi, IL, Ames, D, Lim, ME, Goeree, R. Health technology assessment from a Canadian device industry perspective. J Am Coll Radiol. 2009;6:353–359.CrossRefGoogle ScholarPubMed
6. Hummel, JM, van Rossum, W, Verkerke, GJ, Rakhorst, G. Assessing medical technologies in development; a new paradigm of medical technology assessment. Int J Technol Assess Health Care. 2000;16:1214–1219.CrossRefGoogle ScholarPubMed
7. Ibargoyen-Roteta, N, Gutierrez-Ibarluzea, I, Asua, J, Benguria-Arrate, G, Galnares-Cordero, L. Differences in the identification process for new and emerging health technologies: Analysis of the EuroScan database. Int J Technol Assess Health Care. 2009;25:249–254.CrossRefGoogle ScholarPubMed
8. International Network of Agencies for Health Technology Assessment (INAHTA). http://www.inahta.org/HTA/ (accessed April 16, 2014).Google Scholar
9. Cosh, E, Girling, A, Lilford, R, McAteer, H, Young, T. Investing in new medical technologies: A decision framework. J Commer Biotechnol. 2007;13:263–271.CrossRefGoogle Scholar
10. IJzerman, MJ, Steuten, LMG. Early assessment of medical technologies to inform product development and market access: A review of methods and applications. Appl Health Econ Health Policy. 2011;9:331–347.CrossRefGoogle ScholarPubMed
11. Hilgerink, MP, Hummel, JM, Manohar, S, Vaartjes, SR, IJzerman, MJ. Assessment of the added value of the Twente Photoacoustic Mammoscope in breast cancer diagnosis. Med Dev Evid Res. 2011;4:107–115.CrossRefGoogle ScholarPubMed
12. Van der Wetering, G, Steuten, LMG, von Birgelen, C, Adang, EMM, IJzerman, MJ. Early Bayesian modeling of a potassium lab-on-a-chip for monitoring of heart failure patients at increased risk of hyperkalaemia. Technol Forecast Soc. 2012;79:1268–1279.CrossRefGoogle Scholar
13. Ballini, L, Minozzi, S, Negro, A, Pirini, G, Grilli, R. A method for addressing research gaps in HTA, developed whilst evaluating robotic-assisted surgery: Assisted proposal. Health Res Policy Syst. 2010;8:27–35.CrossRefGoogle ScholarPubMed
14. Girling, A, Young, T, Brown, C, Lilford, R. Early stage valuation of medical devices: The role of developmental uncertainty. Value Health. 2010;13:585–591.CrossRefGoogle ScholarPubMed
15. Ahn, MJ, Zwikael, O, Bednarek, R. Technological invention to product innovation: A project management approach. Int J Proj Manage. 2010;28:559–568.CrossRefGoogle Scholar
16. Hartz, S, John, J. Public health policy decisions on medical innovations: What role can early economic evaluation play? Health Policy. 2009;89:184–192.CrossRefGoogle ScholarPubMed
17. Sculpher, M, Drummond, M, Buxton, M. The iterative use of economic evaluation as part of the process of health technology assessment. J Health Serv Res Policy. 1997;2:26–30.CrossRefGoogle ScholarPubMed
18. Fenwick, E, Palmer, S, Claxton, K, et al. An iterative Bayesian approach to health technology assessment: Application to a policy of preoperative optimization for patients undergoing major elective surgery. Med Decis Making. 2006;26:480–496.CrossRefGoogle ScholarPubMed
19. Pietzsch, JB, Paté-Cornell, ME. Early technology assessment of new medical devices. Int J Technol Assess Health Care. 2008;24:36–44.CrossRefGoogle ScholarPubMed
20. Bragdon, CR, Malchau, H, Yuan, X, et al. Experimental assessment of precision and accuracy of radiostereometric analysis for the determination of polyethylene wear in a total hip replacement model. J Orthop Res. 2002;20:688–695.CrossRefGoogle Scholar
21. Robertson, DG, Watkins, PB, Reily, MD. Metabolomics in toxicology: Preclinical and clinical applications. Toxicol Sci. 2011;120:S146–170.CrossRefGoogle ScholarPubMed
22. Santler, G. The Graz hemisphere splint: A new precise, non-invasive method of replacing the dental arch of 3D-models by plaster models. J Craniomaxillofac Surg. 1998;26:169–173.CrossRefGoogle ScholarPubMed
23. Yao, Z, Huang, K, Guo, J, et al. Screening and determinations of tissue polypeptide antigen by label-free optical immunosensing method. J Nanosci Nanotechnol. 2012;12:112–118.CrossRefGoogle ScholarPubMed
24. Chang, HK, Ishikawa, FN, Zhang, R, et al. Rapid, label-free, electrical whole blood bioassay based on nanobiosensor systems. ACS Nano. 2011;5:9883–9891.CrossRefGoogle ScholarPubMed
25. Schumland, C. Value-added medical-device risk management. IEEE Trans device mater reliabil. 2005;5:488–493.CrossRefGoogle Scholar
26. Alexander, G, Staggers, N. A systematic review of the designs of clinical technology: Findings and recommendations for future research. Adv Nurs Sci. 2009;32:252–279.CrossRefGoogle ScholarPubMed
27. Holtby, H, Skowno, JJ, Kor, DJ, Flick, RP, Uezono, S. New technologies in pediatric anesthesia. Paediatr Anaesth. 2012;22:952–961.CrossRefGoogle ScholarPubMed
28. Cavalcanti, A, Shirinzadeh, B, Kretly, LC. Medical nanorobotics for diabetes control. Nanomedicine. 2008;4:127–138.CrossRefGoogle ScholarPubMed
29. Chávez-Santiago, R, Balasingham, I, Bergsland, J. Ultrawideband technology in medicine: A survey. J Electric Comput Eng. 2012.CrossRefGoogle Scholar
30. Taketani, F, Hara, Y. Characteristics of spherical aberrations in 3 aspheric intraocular lens models measured in a model eye. J Cataract Refract Surg. 2011;37:931–936.CrossRefGoogle Scholar
31. Van Til, J, Renzenbrink, GJ, Groothuis, K, IJzerman, MJ. A preliminary economic evaluation of percutaneous neuromuscular electrical stimulation in the treatment of hemiplegic shoulder pain. Disabil Rehabil. 2006;28:645–651.CrossRefGoogle ScholarPubMed
32. Dougherty, EJ. An evidence-based model comparing the cost-effectiveness of platelet-rich plasma gel to alternative therapies for patients with nonhealing diabetic foot ulcers. Adv Skin Wound Care. 2008; 21:568–575.CrossRefGoogle ScholarPubMed
33. Brodtkorb, TH. Cost-effectiveness analysis of health technologies when evidence is scarce. 2010: Linköping, Sweden: Center for Medical Technology Assessment, Department of Medical and Health Sciences Linköping University.Google Scholar
34. McAteer, H, Cosh, E, Freeman, G, et al. Cost-effectiveness analysis at the development phase of a potential health technology: Examples based on tissue engineering of bladder and urethra. J Tissue Eng Regen Med. 2007;1:343–349.CrossRefGoogle ScholarPubMed
35. Dong, H, Buxton, M. Early assessment of the likely cost-effectiveness of a new technology: A Markov model with probabilistic sensitivity analysis of computer-assisted total knee replacement. Int J Technol Assess Health Care. 2006;22:191–202.CrossRefGoogle ScholarPubMed
36. Pertile, P. An extension of the real option approach to the evaluation of health care technologies: The case of positron emission tomography. Int J Health Care Financ Econ. 2009;9:317–332.CrossRefGoogle Scholar
37. Sculpher, MJ, Claxton, K, Drummond, M. Whither trial-based economic evaluation for health care decision making? Health Econ. 2006;15:677–688.CrossRefGoogle ScholarPubMed
38. Stein, K, Fry, A, Round, A, Milne, R, Brazier, J. What value health? A review of health state values used in early technology assessments for NICE. Appl Health Econ Health Policy. 2005;4:219–228.CrossRefGoogle ScholarPubMed
39. Hartz, S, John, J. Contribution of economic evaluation to decision making in early phases of product development: A methodological and empirical review. Int J Technol Assess Health Care. 2008;24:465–472.CrossRefGoogle ScholarPubMed
40. Persson, J, Brodtkorb, TH, Roback, K. Collaboration between academia, manufacturers and healthcare services for development and adoption of medical devices with regard to costs and effects. IFMBE Proc. 2009;25:138–140.CrossRefGoogle Scholar
41. Tarricone, R, Drummond, M. Challenges in the clinical and economic evaluation of medical devices: The case of transcatheter aortic valve implantation. J Med Marketing. 2011;11:221–229.CrossRefGoogle Scholar
42. Malone, DC, Saverno, KR. Evaluation of a wireless handheld medication management device in the prevention of drug-drug interactions in a medicaid population. J Manag Care Pharm. 2012;18:33–45.Google Scholar
43. Bott, OJ, Hoffmann, I, Bergmann, J, et al. HIS modelling and simulation based cost-benefit analysis of a telemedical system for closed-loop diabetes therapy. Int J Med Inform. 2007;76:S447–455.CrossRefGoogle ScholarPubMed
44. Yen, PY, Bakken, S. Review of health information technology usability study methodologies. J Am Med Inform Assoc. 2012;19:413–422.CrossRefGoogle ScholarPubMed
45. Gallego, G, Bridges, JFP, Flynn, T, Blauvelt, BM, Niessen, LW. Using best-worst scaling in horizon scanning for hepatocellular carcinoma technologies. Int J Technol Assess Health Care. 2012;28:339–346.CrossRefGoogle ScholarPubMed
46. Mazzu, M, Scalvini, S, Giordano, A, et al. Wireless-accessible sensor populations for monitoring biological variables. J Telemed Telecare. 2008;14:135–137.CrossRefGoogle ScholarPubMed
47. Money, AG, Barnett, J, Kuljis, J, et al. The role of the user within the medical device design and development process: Medical device manufacturers’ perspectives. BMC Med Inform Decis Mak. 2011;11:15–26.CrossRefGoogle ScholarPubMed
48. Shah, SGS, Robinson, I. User involvement in healthcare technology development and assessment: Structured literature review. Int J Health Care Qual Assur Inc Leadersh Health Serv. 2006;19:500–515.CrossRefGoogle ScholarPubMed
49. De Rouck, S, Jacobs, A, Leys, M. A methodology for shifting the focus of e-health support design onto user needs: A case in the homecare field. Int J Med Inform. 2008;77:589–601.CrossRefGoogle Scholar
50. Davey, SM, Brennan, M, Meenan, BJ, McAdam, R. Innovation in the medical device sector: An open business model approach for high-tech small firms. Technol Anal Strateg Manage J. 2011;23:807–824.CrossRefGoogle Scholar
51. Sanders, PMH, IJzerman, MJ, Roach, MJ, Gustafson, KJ. Patient preferences for next generation neural prostheses to restore bladder function. Spinal Cord. 2010;49:113–119.CrossRefGoogle ScholarPubMed
52. LeRouge, C, Ma, J, Sneha, S, Tolle, K. User profiles and personas in the design and development of consumer health technologies. Int J Med Inform. 2013;82:251–268.CrossRefGoogle ScholarPubMed
53. Cytryn, KN, Patel, VL. Reasoning about diabetes and its relationship to the use of telecommunication technology by patients and physicians. Int J Med Inform. 1998;51:137–151.CrossRefGoogle Scholar
54. Coughlin, JF, Pope, JE, Leedle, BR. Older adult perceptions of smart home technologies: Implications for research, policy & market innovations in healthcare. Conf Proc IEEE Eng Med Biol Soc. 2007;1810–1815.CrossRefGoogle Scholar
55. Shah, SG, Robinson, I, AlShawi, S. Developing medical device technologies from users’ perspectives: A theoretical framework for involving users in the development process. Int J Technol Assess Health Care. 2009;25:514–521.CrossRefGoogle ScholarPubMed
56. Alnanih, R, Radhakrishnan, T, Ormandjieva, O. Characterising context for mobile user interfaces in health care applications. Procedia Comput Sci. 2012;10:1086–1093.CrossRefGoogle Scholar
57. Sintonen, S, Immonen, M. Telecare services for aging people: Assessment of critical factors influencing the adoption intention. Comp Hum Behav. 2013;29:1307–1317.CrossRefGoogle Scholar
58. Bridgelal Ram, M, Grocott, PR, Weir, H. Issues and challenges of involving users in medical device development. Health Expect. 2008;11:63–71.CrossRefGoogle ScholarPubMed
59. Hardisty, AR, Peirce, SC, Preece, A, et al. Bridging two translation gaps: A new informatics research agenda for telemonitoring of chronic disease. Int J Med Inform. 2011;80:734–744.CrossRefGoogle ScholarPubMed
60. Shah, SGS, Robinson, I. Benefits of and barriers to involving users in medical device technology development and evaluation. Int J Technol Assess Health Care. 2007;23:131–137.CrossRefGoogle ScholarPubMed
61. Robinson, DKR. Co-evolutionary scenarios: An application to prospecting futures of the responsible development of nanotechnology. Technol Forecast Soc. 2009;76:1222–1239.CrossRefGoogle Scholar
62. Retèl, VP, Hummel, JM, van Harten, WH. Review on early technology assessments of nanotechnologies in oncology. Mol Oncol. 2009;3:394–401.CrossRefGoogle ScholarPubMed
63. Hummel, JM, Boomkamp, ISM, Steuten, LMG, Verkerke, BGJ, IJzerman, MJ. Predicting the health economic performance of new non-fusion surgery in adolescent idiopathic scoliosis. J Orthopaed Res. 2012;30:1453–1458.CrossRefGoogle ScholarPubMed
64. Reis, J, McGinty, B, Jones, S. An e-learning caregiving program for prostate cancer patients and family members. J Med Syst. 2003;27:1–12.CrossRefGoogle ScholarPubMed
65. Retèl, VP, Joore, MA, Linn, SC, Rutgers, EJ, Van Harten, WH. Scenario drafting to anticipate future developments in technology assessment. BMC Res Notes. 2012;5:442–453.CrossRefGoogle ScholarPubMed
66. Robinson, DKR, Huang, L, Guo, Y, Porter, AL. Forecasting Innovation Pathways (FIP) for new and emerging science and technologies. Technol Forecast Soc. 2013;80:267–285.CrossRefGoogle Scholar
67. Hummel, JM, van Rossum, W, Verkerke, GJ, Rakhorst, G. Medical technology assessment: The use of the analytic hierarchy process as a tool for multidisciplinary evaluation of medical devices. Int J Artif Organs. 2000;23:782–787.CrossRefGoogle ScholarPubMed
68. Martin, H, Daim, TU. Technology roadmap development process (TRDP) for the service sector: A conceptual framework. Technol Soc. 2012;34:94–105.CrossRefGoogle Scholar
69. Schaeffer, NE. The role of human factors in the design and development of an insulin pump. J Diabetes Sci Technol. 2012;6:260–264.CrossRefGoogle ScholarPubMed
70. Robinson, DKR, Propp, T. Multi-path mapping for alignment strategies in emerging science and technologies. Technol Forecast Soc. 2008;75:517–538.CrossRefGoogle Scholar
71. Kazanjian, A, Green, CJ. Beyond effectiveness: The evaluation of information systems using a comprehensive health technology assessment framework. Comput Biol Med. 2002;32:165–177.CrossRefGoogle ScholarPubMed
72. Rogowski, WH, Hartz, SC, John, JH. Clearing up the hazy road from bench to bedside: A framework for integrating the fourth hurdle into translational medicine. BMC Health Serv Res. 2008;8:194–205.CrossRefGoogle ScholarPubMed
73. Stevens, A, Milne, R, Lilford, R, Gabbay, J. Keeping pace with new technologies: Systems needed to identify and evaluate them. Br Med J. 1999;319:1291–1294.CrossRefGoogle Scholar
74. Spiegelhalter, DJ, Myles, JP, Jones, DR, Abrams, KR. Bayesian methods in health technology assessment: A review. Health Technol Assess. 2000;4:1–130.CrossRefGoogle ScholarPubMed
75. Stone, VI, Lane, JP. Modeling technology innovation: How science, engineering, and industry methods can combine to generate beneficial socioeconomic impacts. Implement Sci. 2012;7:44.CrossRefGoogle ScholarPubMed
76. Ladabaum, U, Brill, JV, Sonnenberg, A. How to value technological innovation: A proposal for determining relative clinical value. Gastroenterology. 2013;144:5–8.CrossRefGoogle ScholarPubMed
77. Tal, O, Hakak, N. Early awareness and alert systems for medical technologies in Israel. Int J Technol Assess Health Care. 2012;28:333–338.CrossRefGoogle ScholarPubMed
78. Douw, K, Vondeling, H, Oortwijn, W. Priority setting for horizon scanning of new health technologies in Denmark: Views of health care stakeholders and health economists. Health Policy. 2006;76:334–345.CrossRefGoogle ScholarPubMed
79. Oortwijn, WJ, Vondeling, H, van Barneveld, T, van Vugt, C, Bouter, LM. Priority setting for health technology assessment in The Netherlands: Principles and practice. Health Policy. 2002;62:227–242.CrossRefGoogle ScholarPubMed
80. Brown, IT, Smale, A, Verma, A, Momandwall, S. Medical technology horizon scanning. Australas Phys Eng Sci Med. 2005;28:200–203.CrossRefGoogle ScholarPubMed
81. Wild, C, Langer, T. Emerging health technologies: Informing and supporting health policy early. Health Policy. 2008;87:160–171.CrossRefGoogle ScholarPubMed
82. Douw, K, Vondeling, H, Sørensen, J, Jørgensen, T, Sigmund, H. “The future should not take us by surprise”: Preparation of an early warning system in Denmark. Int J Technol Assess Health Care. 2004;20:342–350.CrossRefGoogle ScholarPubMed
83. Berry, DA. Introduction to Bayesian methods III: Use and interpretation of Bayesian tools in design and analysis. Clin Trials. 2005;2:295–300.CrossRefGoogle ScholarPubMed
84. O'Malley, SP, Jordan, E. Horizon scanning of new and emerging medical technology in Australia: Its relevance to medical services advisory committee health technology assessments and public funding. Int J Technol Assess Health Care. 2009;25:374–382.CrossRefGoogle ScholarPubMed
85. Postmus, D, de Graaf, G, Hillege, HL, Steyerberg, EW, Buskens, E. A method for the early health technology assessment of novel biomarker measurement in primary prevention programs. Stat Med. 2012;31:2733–2744.CrossRefGoogle ScholarPubMed
86. Laking, GR, Price, PM, Sculpher, MJ. Assessment of the technology for functional imaging in cancer. Eur J Cancer. 2002;38:2194–2199.CrossRefGoogle ScholarPubMed
87. Okamoto, E, Hashimoto, T, Inoue, T, Mitamura, Y. Blood compatible design of a pulsatile blood pump using computational fluid dynamics and computer-aided design and manufacturing technology. Artif Organs. 2003;27:61–67.CrossRefGoogle ScholarPubMed
88. Clopton, BM, Spelman, FA. Technology and the future of cochlear implants. Ann Otol Rhinol Laryngol Suppl. 2003;191:26–32.CrossRefGoogle ScholarPubMed
89. Fukamachi, K. New technologies for mechanical circulatory support: Current status and future prospects of CorAide and MagScrew technologies. J Artif Organs. 2004;7:45–57.CrossRefGoogle ScholarPubMed
90. Chang, WC, Sretavan, DW. Microtechnology in medicine: The emergence of surgical microdevices. Clin Neurosurg. 2007;54:137–147.Google Scholar
91. Cannesson, M, Rinehart, J. Innovative technologies applied to anesthesia: How will they impact the way clinicians practice? J Cardiothorac Vasc Anesth. 2012;26:711–720.CrossRefGoogle ScholarPubMed
92. Carrara, S. Nano-bio-technology and sensing chips: New systems for detection in personalized therapies and cell biology. Sensors. 2010;10:526–543.CrossRefGoogle ScholarPubMed
93. Chatterjee, C, Srinivasan, V. Ethical issues in health care sector in India. IIMB Manag Rev. 2013; 25:49–62.CrossRefGoogle Scholar
94. Edelmuth, RCL, Nitsche, MA, Battistella, L, Fregni, F. Why do some promising brain-stimulation devices fail the next steps of clinical development? Exp Rev Med Dev. 2010;7:67–97.CrossRefGoogle ScholarPubMed
95. Edwards, B. The future of hearing aid technology. Trends Amplif. 2007;11:31–45.CrossRefGoogle ScholarPubMed
96. Elhawary, H, Tse, ZT, Hamed, A, et al. The case for MR-compatible robotics: A review of the state of the art. Int J Med Robot. 2008;4:105–113.CrossRefGoogle ScholarPubMed
97. Gervais, L, De Rooij, N, Delamarche, E. Microfluidic chips for point-of-care immunodiagnostics. Adv Mater. 2011;23:H151–176.Google ScholarPubMed
98. Granger, BB, Bosworth, HB. Medication adherence: Emerging use of technology. Curr Opin Cardiol. 2011;26:279–287.CrossRefGoogle ScholarPubMed
99. Habash, RWY, Bansal, R, Krewski, D, Alhafid, HT. Thermal therapy, Part III: Ablation techniques. Crit Rev Biomed Eng. 2007;35:37–121.CrossRefGoogle ScholarPubMed
100. Holloway, CMB, Easson, A, Escallon, J. Technology as a force for improved diagnosis and treatment of breast disease. Can J Surg. 2010;53:268–277.Google ScholarPubMed
101. Hovorka, R. Closed-loop insulin delivery: From bench to clinical practice. Nat Rev Endocrinol. 2011;7:385–395.CrossRefGoogle ScholarPubMed
102. Lavee, J, Paz, Y. Mechanical alternatives to the human heart: Future devices. Isr Med Assoc J. 2002;4:290–293.Google Scholar
103. McMullan, JT, Knight, WA, Clark, JF, Beyette, FR, Pancioli, A. Time-critical neurological emergencies: The unfulfilled role for point-of-care testing. Int J Emerg Med. 2010;3:127–131.CrossRefGoogle ScholarPubMed
104. Micera, S, Bonato, P, Tamura, T. Gerontechnology. IEEE Eng Med Biol Mag. 2008;27:10–14.CrossRefGoogle ScholarPubMed
105. Najarian, S, Fallahnezhad, M, Afshari, E. Advances in medical robotic systems with specific applications in surgery-a review. J Med Engine Technol. 2011;35:19–33.CrossRefGoogle ScholarPubMed
106. Patel, DN, Bailey, SR. Nanotechnology in cardiovascular medicine. Catheter Cardiovasc Interv. 2007;69:643–654.CrossRefGoogle ScholarPubMed
107. Percevic, R, Lambert, MJ, Kordy, H. Computer-supported monitoring of patient treatment response. J Clin Psychol. 2004;60:285–299.CrossRefGoogle ScholarPubMed
108. Peterhans, M, Oliveira, T, Banz, V, Candinas, D, Weber, S. Computer-assisted liver surgery: Clinical applications and technological trends. Crit Rev Biomed Eng. 2012;40:199–220.CrossRefGoogle ScholarPubMed
109. Pfister, BJ, Gordon, T, Loverde, JR, et al. Biomedical engineering strategies for peripheral nerve repair: Surgical applications, state of the art, and future challenges. Crit Rev Biomed Eng. 2011;39:81–124.CrossRefGoogle ScholarPubMed
110. Postma, TJ, Alers, JC, Terpstra, S, Zuurbier, A. The future of imaging techniques for cancer patients in the Netherlands: A Delphi study. Eur J Health Econ. 2006;7:117–122.CrossRefGoogle ScholarPubMed
111. Prager, RW, Ljaz, UZ, Gee, AH, Treece, GM. Three-dimensional ultrasound imaging. Proc Inst Mech Eng H. 2010;224:193–223.CrossRefGoogle ScholarPubMed
112. Rosengart, TK, Feldman, TC, Borger, MA, et al. Percutaneous and minimally invasive valve procedures: A scientific statement from the American Heart Association Council on Cardiovascular Surgery and Anesthesia, Council on Clinical Cardiology, Functional Genomics and Translational Biology Interdisciplinary Working Group, and Quality of Care and Outcomes Research Interdisciplinary Working Group. Circulation. 2008;117:1750–1767.CrossRefGoogle Scholar
113. Russell-Minda, E, Jutai, J, Speechley, M, et al. Health technologies for monitoring and managing diabetes: A systematic review. J Diabetes Sci Technol. 2009;3:1460–1471.CrossRefGoogle ScholarPubMed
114. Sahandi, R, Noroozi, S, Roushan, G, Heaslip, V, Liu, Y. Wireless technology in the evolution of patient monitoring on general hospital wards. J Med Eng Technol. 2010;34:51–63.CrossRefGoogle ScholarPubMed
115. Scherer, MJ, Hart, T, Kirsch, N, Schulthesis, M. Assistive technologies for cognitive disabilities. Crit Rev Phys Rehabil Med. 2005;17:195–215.CrossRefGoogle Scholar
116. Schleyer, T, Mattsson, U, Ni Riordain, R, et al. Advancing oral medicine through informatics and information technology: A proposed framework and strategy. Oral Dis. 2011;17:85–94.CrossRefGoogle ScholarPubMed
117. Baumgart, DC. Personal digital assistants in health care: Experienced clinicians in the palm of your hand? Lancet. 2005;366:1210–1222.CrossRefGoogle ScholarPubMed
118. Azari, A, Nikzad, S. Computer-assisted implantology: Historical background and potential outcomes - A review. Int J Med Robot. 2008;4:95–104.CrossRefGoogle ScholarPubMed
119. Paradise, J, Diliberto, GM, Tisdale, AW, Kokkoli, E. Exploring emerging nanobiotechnology drugs and medical devices. Food Drug Law J. 2008;63:407–420.Google ScholarPubMed
120. Backhouse, ME, Wonder, M, Hornby, E. Early dialogue between the developers of new technologies and pricing and reimbursement agencies: A pilot study. Value Health. 2011;14:608–615.CrossRefGoogle ScholarPubMed
121. De Rouck, S, Jacobs, A, Leys, M. A methodology for shifting the focus of e-health support design onto user needs: A case in the homecare field. Int J Med Inform. 2008;77:589–601.CrossRefGoogle Scholar
122. Goeree, R, Levin, L, Chandra, K, et al. Health technology assessment and primary data collection for reducing uncertainty in decision making. J Am Coll Radiol. 2009;6:332–342.CrossRefGoogle ScholarPubMed
123. Abalos, E, Carroli, G, Mackey, ME. The tools and techniques of evidence-based medicine. Best Pract Res Clin Obstet Gynaecol. 2005;19:15–26.CrossRefGoogle ScholarPubMed
124. Bojke, L, Claxton, K, Sculpher, M, Palmer, S. Characterizing structural uncertainty in decision analytic models: A review and application of methods. Value Health. 2009;12:739–749.CrossRefGoogle ScholarPubMed
Supplementary material: File

Markiewicz Supplementary Material

Tables

Download Markiewicz Supplementary Material(File)
File 75.8 KB