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  • Katarzyna Markiewicz (a1), Janine A. van Til (a1) and Maarten J. IJzerman (a1)


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.



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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.
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;6:299306.
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;24:459464.
4. Goodman, CS, Ahn, R. Methodological approaches of health technology assessment. Int J Med Inform. 1999;56:97105.
5. Ferrusi, IL, Ames, D, Lim, ME, Goeree, R. Health technology assessment from a Canadian device industry perspective. J Am Coll Radiol. 2009;6:353359.
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;16:12141219.
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;25:249254.
8. International Network of Agencies for Health Technology Assessment (INAHTA). (accessed April 16, 2014).
9. Cosh, E, Girling, A, Lilford, R, McAteer, H, Young, T. Investing in new medical technologies: A decision framework. J Commer Biotechnol. 2007;13:263271.
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;9:331347.
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;4:107115.
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;79:12681279.
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;8:2735.
14. Girling, A, Young, T, Brown, C, Lilford, R. Early stage valuation of medical devices: The role of developmental uncertainty. Value Health. 2010;13:585591.
15. Ahn, MJ, Zwikael, O, Bednarek, R. Technological invention to product innovation: A project management approach. Int J Proj Manage. 2010;28:559568.
16. Hartz, S, John, J. Public health policy decisions on medical innovations: What role can early economic evaluation play? Health Policy. 2009;89:184192.
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;2:2630.
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;26:480496.
19. Pietzsch, JB, Paté-Cornell, ME. Early technology assessment of new medical devices. Int J Technol Assess Health Care. 2008;24:3644.
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;20:688695.
21. Robertson, DG, Watkins, PB, Reily, MD. Metabolomics in toxicology: Preclinical and clinical applications. Toxicol Sci. 2011;120:S146170.
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;26:169173.
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;12:112118.
24. Chang, HK, Ishikawa, FN, Zhang, R, et al. Rapid, label-free, electrical whole blood bioassay based on nanobiosensor systems. ACS Nano. 2011;5:98839891.
25. Schumland, C. Value-added medical-device risk management. IEEE Trans device mater reliabil. 2005;5:488493.
26. Alexander, G, Staggers, N. A systematic review of the designs of clinical technology: Findings and recommendations for future research. Adv Nurs Sci. 2009;32:252279.
27. Holtby, H, Skowno, JJ, Kor, DJ, Flick, RP, Uezono, S. New technologies in pediatric anesthesia. Paediatr Anaesth. 2012;22:952961.
28. Cavalcanti, A, Shirinzadeh, B, Kretly, LC. Medical nanorobotics for diabetes control. Nanomedicine. 2008;4:127138.
29. Chávez-Santiago, R, Balasingham, I, Bergsland, J. Ultrawideband technology in medicine: A survey. J Electric Comput Eng. 2012.
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;37:931936.
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;28:645651.
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; 21:568575.
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.
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;1:343349.
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;22:191202.
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;9:317332.
37. Sculpher, MJ, Claxton, K, Drummond, M. Whither trial-based economic evaluation for health care decision making? Health Econ. 2006;15:677688.
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;4:219228.
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;24:465472.
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;25:138140.
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;11:221229.
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;18:3345.
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:S447455.
44. Yen, PY, Bakken, S. Review of health information technology usability study methodologies. J Am Med Inform Assoc. 2012;19:413422.
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;28:339346.
46. Mazzu, M, Scalvini, S, Giordano, A, et al. Wireless-accessible sensor populations for monitoring biological variables. J Telemed Telecare. 2008;14:135137.
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;11:1526.
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;19:500515.
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;77:589601.
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;23:807824.
51. Sanders, PMH, IJzerman, MJ, Roach, MJ, Gustafson, KJ. Patient preferences for next generation neural prostheses to restore bladder function. Spinal Cord. 2010;49:113119.
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;82:251268.
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;51:137151.
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.
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;25:514521.
56. Alnanih, R, Radhakrishnan, T, Ormandjieva, O. Characterising context for mobile user interfaces in health care applications. Procedia Comput Sci. 2012;10:10861093.
57. Sintonen, S, Immonen, M. Telecare services for aging people: Assessment of critical factors influencing the adoption intention. Comp Hum Behav. 2013;29:13071317.
58. Bridgelal Ram, M, Grocott, PR, Weir, H. Issues and challenges of involving users in medical device development. Health Expect. 2008;11:6371.
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;80:734744.
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;23:131137.
61. Robinson, DKR. Co-evolutionary scenarios: An application to prospecting futures of the responsible development of nanotechnology. Technol Forecast Soc. 2009;76:12221239.
62. Retèl, VP, Hummel, JM, van Harten, WH. Review on early technology assessments of nanotechnologies in oncology. Mol Oncol. 2009;3:394401.
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;30:14531458.
64. Reis, J, McGinty, B, Jones, S. An e-learning caregiving program for prostate cancer patients and family members. J Med Syst. 2003;27:112.
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;5:442453.
66. Robinson, DKR, Huang, L, Guo, Y, Porter, AL. Forecasting Innovation Pathways (FIP) for new and emerging science and technologies. Technol Forecast Soc. 2013;80:267285.
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;23:782787.
68. Martin, H, Daim, TU. Technology roadmap development process (TRDP) for the service sector: A conceptual framework. Technol Soc. 2012;34:94105.
69. Schaeffer, NE. The role of human factors in the design and development of an insulin pump. J Diabetes Sci Technol. 2012;6:260264.
70. Robinson, DKR, Propp, T. Multi-path mapping for alignment strategies in emerging science and technologies. Technol Forecast Soc. 2008;75:517538.
71. Kazanjian, A, Green, CJ. Beyond effectiveness: The evaluation of information systems using a comprehensive health technology assessment framework. Comput Biol Med. 2002;32:165177.
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;8:194205.
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;319:12911294.
74. Spiegelhalter, DJ, Myles, JP, Jones, DR, Abrams, KR. Bayesian methods in health technology assessment: A review. Health Technol Assess. 2000;4:1130.
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.
76. Ladabaum, U, Brill, JV, Sonnenberg, A. How to value technological innovation: A proposal for determining relative clinical value. Gastroenterology. 2013;144:58.
77. Tal, O, Hakak, N. Early awareness and alert systems for medical technologies in Israel. Int J Technol Assess Health Care. 2012;28:333338.
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;76:334345.
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;62:227242.
80. Brown, IT, Smale, A, Verma, A, Momandwall, S. Medical technology horizon scanning. Australas Phys Eng Sci Med. 2005;28:200203.
81. Wild, C, Langer, T. Emerging health technologies: Informing and supporting health policy early. Health Policy. 2008;87:160171.
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;20:342350.
83. Berry, DA. Introduction to Bayesian methods III: Use and interpretation of Bayesian tools in design and analysis. Clin Trials. 2005;2:295300.
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;25:374382.
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;31:27332744.
86. Laking, GR, Price, PM, Sculpher, MJ. Assessment of the technology for functional imaging in cancer. Eur J Cancer. 2002;38:21942199.
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;27:6167.
88. Clopton, BM, Spelman, FA. Technology and the future of cochlear implants. Ann Otol Rhinol Laryngol Suppl. 2003;191:2632.
89. Fukamachi, K. New technologies for mechanical circulatory support: Current status and future prospects of CorAide and MagScrew technologies. J Artif Organs. 2004;7:4557.
90. Chang, WC, Sretavan, DW. Microtechnology in medicine: The emergence of surgical microdevices. Clin Neurosurg. 2007;54:137147.
91. Cannesson, M, Rinehart, J. Innovative technologies applied to anesthesia: How will they impact the way clinicians practice? J Cardiothorac Vasc Anesth. 2012;26:711720.
92. Carrara, S. Nano-bio-technology and sensing chips: New systems for detection in personalized therapies and cell biology. Sensors. 2010;10:526543.
93. Chatterjee, C, Srinivasan, V. Ethical issues in health care sector in India. IIMB Manag Rev. 2013; 25:4962.
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;7:6797.
95. Edwards, B. The future of hearing aid technology. Trends Amplif. 2007;11:3145.
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;4:105113.
97. Gervais, L, De Rooij, N, Delamarche, E. Microfluidic chips for point-of-care immunodiagnostics. Adv Mater. 2011;23:H151176.
98. Granger, BB, Bosworth, HB. Medication adherence: Emerging use of technology. Curr Opin Cardiol. 2011;26:279287.
99. Habash, RWY, Bansal, R, Krewski, D, Alhafid, HT. Thermal therapy, Part III: Ablation techniques. Crit Rev Biomed Eng. 2007;35:37121.
100. Holloway, CMB, Easson, A, Escallon, J. Technology as a force for improved diagnosis and treatment of breast disease. Can J Surg. 2010;53:268277.
101. Hovorka, R. Closed-loop insulin delivery: From bench to clinical practice. Nat Rev Endocrinol. 2011;7:385395.
102. Lavee, J, Paz, Y. Mechanical alternatives to the human heart: Future devices. Isr Med Assoc J. 2002;4:290293.
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;3:127131.
104. Micera, S, Bonato, P, Tamura, T. Gerontechnology. IEEE Eng Med Biol Mag. 2008;27:1014.
105. Najarian, S, Fallahnezhad, M, Afshari, E. Advances in medical robotic systems with specific applications in surgery-a review. J Med Engine Technol. 2011;35:1933.
106. Patel, DN, Bailey, SR. Nanotechnology in cardiovascular medicine. Catheter Cardiovasc Interv. 2007;69:643654.
107. Percevic, R, Lambert, MJ, Kordy, H. Computer-supported monitoring of patient treatment response. J Clin Psychol. 2004;60:285299.
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;40:199220.
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;39:81124.
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;7:117122.
111. Prager, RW, Ljaz, UZ, Gee, AH, Treece, GM. Three-dimensional ultrasound imaging. Proc Inst Mech Eng H. 2010;224:193223.
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;117:17501767.
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;3:14601471.
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;34:5163.
115. Scherer, MJ, Hart, T, Kirsch, N, Schulthesis, M. Assistive technologies for cognitive disabilities. Crit Rev Phys Rehabil Med. 2005;17:195215.
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;17:8594.
117. Baumgart, DC. Personal digital assistants in health care: Experienced clinicians in the palm of your hand? Lancet. 2005;366:12101222.
118. Azari, A, Nikzad, S. Computer-assisted implantology: Historical background and potential outcomes - A review. Int J Med Robot. 2008;4:95104.
119. Paradise, J, Diliberto, GM, Tisdale, AW, Kokkoli, E. Exploring emerging nanobiotechnology drugs and medical devices. Food Drug Law J. 2008;63:407420.
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;14:608615.
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;77:589601.
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;6:332342.
123. Abalos, E, Carroli, G, Mackey, ME. The tools and techniques of evidence-based medicine. Best Pract Res Clin Obstet Gynaecol. 2005;19:1526.
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;12:739749.


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  • Katarzyna Markiewicz (a1), Janine A. van Til (a1) and Maarten J. IJzerman (a1)


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