19-AM-10: ST1.4 - Why Is It Difficult to Implement E-Health Innovation?
E-health encompasses a set of technological innovations (e.g. telemedecine, AI and algorithms, Medical devices) that announces a deep transformation of healthcare delivery systems. While there are many reasons to consider important improvements in terms of quality of care and efficiency, their implementation process also reveals unexpected barriers. Among them can be mentioned different patient’s behaviors for adopting them, power strategies among healthcare professionals that limit their effective use, and a lack of robust method for assessing their added-value.
Based on empirical and theoretical studies, this track aims to develop an analysis of the barriers during the implementation process of a technological innovation.
Articles that lead to define managerial recommendations when implementing e-health innovations are welcoming.
Electronic Medical Records: For Good or Ill? Data Breaches in the US Healthcare Ecosystem
TSM-Research, Université Toulouse Capitole, CNRS
Electronic Medical Records (EMRs) are the most significant innovation in digital health. Their implementation allows both improved patient care and reduced health cost, and facilitates medical decision-making.
Though controversial, Electronic Medical Records (EMRs) are the most significant innovation in digital health (Angst, Agarwal, Sambamurthy, & Kelley, 2010). EMRs centralize patient data and make it accessible to different actors of the health ecosystem in real time (Mishra, Anderson, Angst, & Agarwal, 2012). Adopting EMRs leads to improved patient care (Angst et al., 2010) and reduced health cost (Bhargava & Mishra, 2014). However, the number of data breaches is increasing worldwide. The more patient data are shared between different healthcare organizations (e.g., between healthcare providers and health plans), the higher the chances of an attack. In the US, health data breaches represent up to a quarter of all data breaches. The major issue with EMRs is that their current anonymization techniques are not effective (Li & Qin, 2017). Patients are so increasingly concerned by their privacy that it can lead to the rejection of EMRs.
Surprisingly, the literature under-investigates health data breaches aside from healthcare providers.
Since healthcare organizations (i.e., healthcare providers, business associates, and health plans) share patients’ data between each other, it is critical to encompass the whole healthcare eco-system in academic investigations.
Our exploratory study aims to classify EMRs breaches in the US healthcare eco-system. We ran a TwoStep cluster analysis. It fits best for mixed data thanks to its Log-likelihood distance measure. In addition, the Bayesian’s information criterion allows an automatic identification of the most appropriate number of clusters. TwoStep cluster analysis is well suited to managerial decision making in exploratory designs and more reliable than other cluster analyses such as k-means classification.
We used the publicly available data of the U.S. Department of Health and Human Services Office for Civil Rights. We selected the period in which data is complete (i.e., from 2010 to 2017). The final sample is composed of 139 EMRs breaches. We implemented cluster analysis with three variables: the number of harmed patients in a given breach, as a proxy of the scope of the attack; the origin of that EMRs breach (e.g., disclosure/unauthorized access or hacking); and the type of the attacked healthcare organization (i.e., healthcare providers, business associates and health plans).
Descriptive data analysis shows that EMRs breaches are more frequent overtime: in 2017, they represent more than 10% of all data breaches in the US healthcare system. TwoStep cluster analysis puts forward four distinct clusters.
The content of the four clusters is reported in Table 2. Three of them exclusively relates to healthcare providers. Their number of EMRs breaches is the most important, ranging from 29 to 72. These clusters are distinguished by the origin of the breaches (i.e., disclosure/unauthorized access, hacking, and theft). Despite the high number of EMRs breaches, the number of harmed patients remains relatively moderate (i.e., between around 6 000 and 11 000).
The fourth and last cluster appears to be the most important one for our exploratory study. In contrast with the previous clusters, it is composed of business associates and health plans, excluding healthcare providers. The origins of EMRs breaches are diverse (i.e., disclosure/unauthorized access, hacking, theft, and, last but not least, loss). Despite the small number of EMRs breaches (i.e., 8), the average number of harmed patients is incredibly high (i.e., about 800 000). Surprisingly, to the best of our knowledge, the literature under-investigates EMRs breaches out of healthcare providers.
Contribution to Scholarship
The literature emphasizes data breaches in hospitals (i.e., a type of healthcare providers) and, hence, little is known about such breaches in a healthcare ecosystem. In our exploratory study, we show that all types of healthcare organizations (i.e., healthcare providers, business associates, and health plans) suffer from EMRSs breaches. The results of our classification analysis put forward a cluster that is exclusively composed of business associates and health plans. ). Despite their small number of EMRs breaches, the average number of harmed patients is incredibly high. In the light of neo-institutional theory, this raises a major issue: do business associates and health plans adopt IT security features symbolically?
Contribution to Practice
Professionals who use EMRs have diverse profiles, with heterogeneous levels of digital literacy, making the implementation of EMRs slow. An implementation failure can cost dozens of million dollars in damage. Executive education in IT for managers is thus more than ever necessary. Healthcare organizations could also assess the digital literacy of their members (e.g., practitioners and administrative staff) before implementing EMRs and invest for their training. Such training should be adapted to the diverse profiles of the healthcare organizations’ members. For instance, one’s might make health plans aware of data loss or make practitioners aware of involuntary disclosure/unauthorized access.
This study fits the track 1.4. because it deals with one of the major e-health innovations: electronic medical records.
Angst, C. M., Agarwal, R., Sambamurthy, V., & Kelley, K. (2010). Social Contagion and Information Technology Diffusion: The Adoption of Electronic Medical Records in U.S. Hospitals. Management Science, 56(8), 1219–1241. http://doi.org/10.1287/mnsc.1100.1183
Bhargava, H. K., & Mishra, A. N. (2014). Electronic Medical Records and Physician Productivity: Evidence from Panel Data Analysis. Management Science, 60(10), 2543–2562. http://doi.org/10.1287/mnsc.2014.1934
Li, X.-B., & Qin, J. (2017). Anonymizing and Sharing Medical Text Records. Information Systems Research, 28(2), 332–352. http://doi.org/10.1287/isre.2016.0676
Mishra, A. N., Anderson, C., Angst, C. M., & Agarwal, R. (2012). Electronic Health Records Assimilation and Physician Identity Evolution: An Identity Theory Perspective. Information Systems Research, 23(3), 738–760. http://doi.org/10.1287/isre.1110.0407
Leveraging big data to drive innovation: the case of big data platforms in French academic health centers
The French healthcare system is at the dawn of a major reform which is challenging the positioning and role of French AHCs. In this context, data analytics platforms become strategic assets for French AHCs as they can help them differentiate from other providers and define their future organizational identity.
This research covers the resource-based view of data analytics and the application of data analytics to the innovation process.
It will be applied to the case of data analytics platforms in French AHCs and will be focused on the knowledge mobilization phase of the innovation process.
Relevant literature is:
On Academic Health Centers and innovation
Dzau VJ, Yoediono Z, Ellaissi WF, Cho AH. Fostering innovation in medicine and health care: what must academic health centers do? Acad Med J Assoc Am Med Coll. 2013;88:1424 9.
On Data analytics platform and care delivery
Paulus R, Davis K, Steele G. Continuous Innovation In Health Care: Implications Of The Geisinger Experience. Health Affairs, 27, no.5 (2008):1235-1245
On Resource-based view
Barney, J. (1991). Firm resources and sustained competitive advantage. Journal of Management, 17(1), 99–120.
Resource based view data analytics
On Knowledge mobilization
Dougherty D. Organizing Practices in Services: Capturing Practice-Based Knowledge for Innovation. Strateg Organ. 2004;2:35 64.
In recent literature, the RBV model has been applied to data analytics, but there is no specific work on the healthcare sector. The case of DAP in French AHCs will offer us the opportunity to fill this gap.
This work is addressing the two following research questions:
- How do French AHCs leverage their DAP as strategic assets to differentiate themselves from other providers?
- How do DAP impact the knowledge mobilization dynamics and the overall innovation process in care delivery?
As a preliminary work, we will perform a literature review of the scientific work done on the two conceptual frameworks. We will use search on EBSCO and CAIRN.
The methodological strategy will be a case study design.
S1 : exploratory study to profile AHCs based on their DAP strategy and to select 2 institutions for in-depth case study.
S2 : comparative case study on knowledge mobilization to detail the dynamics of the emergence of data-driven care delivery innovation.
S3 : recommendations elaboration to AHC managers on how to maximize the potential for care delivery innovation
The data in stage 1 (exploratory case study) will be:
1) Face/face interviews with 1 key informant from the AHC CEO, CMO and CRO communities (3 interviews)
2) Telephone interview with 1 informant from each AHC selected using a snowball technique (targeted: CEO or member of the senior leadership team) (29 interviews)
3) Policy and internal documents (request during telephone interviews)
The data in stage 2 (comparative case study) will be :
1 day of on-site semi-structured interviews with key informant for each selected AHCs (up to 8 interviews per day) (16 interviews)
The data in stage 3 will be:
Interviews with experts to enrich and validate recommendations (4 interviews)
By investing in DAPs, French AHCs primarily want to increase the competitiveness of their research and clinical activities by improving the value generated from existing databases. If the ambition is shared by all, the integration of DAPs within the organization and their impact on the innovation process are very variable.
At this date, the main benefits from investing in DAPs may not reside in IT infrastructure itself, but in the change of image and positioning. Through their investments in DAPs, AHCs acquire intangible assets that change their position towards their competitors, HCPs and patients. Becoming a “big data innovator” conveys positive signals that impact the perception of the performance of clinical activities, quality of care, patient safety, work conditions.
These intangible assets are drivers of change in clinical and research practices. The association of a new IT infrastructure with a new image appears to be a key success factor for its implementation at the organizational level.
Contribution to Scholarship
The ideas developed in this research help understand how AHCs – and care providers in general - can acquire value from their investments in data analytics infrastructure. We have used the RBV to develop a framework for identifying how data analytics infrastructure can influence the transformation of AHCs.
This research makes an original contribution to scholarship through the application of the RBV framework to the healthcare sector, and more specifically AHCs. In a context of reform in most healthcare systems, the question of competitive advantage will apply.
As for knowledge mobilization, the present research opens new perspectives by focusing on care delivery innovation, which engages a scope of actors much larger than what has been studied in discovery or diagnostic innovation.
Contribution to Practice
This research can help AHCs managers and national decision-makers to better understand how data analytics platforms should be implemented to maximize the impact on innovation in care delivery.
The impact of these platforms does not reside in its infrastructure but its integration in the organization. Positioning the use of data analytics as a strategic ambition and communicating this one to external stakeholders appears to be drivers for its integration and a trigger for changes in clinical and research practices.
ehealth implementation can not limit itself to setting up an infrastructure or adding a solution to a user interface. ehealth innovation realizes its potential when fully integrated into strategy and practices. Successfully engaging actors can be achieved by leveraging intangible assets.
Atlas des SIH 2018. 2018. Ministère des Solidarités et de la Santé. https://solidarites-sante.gouv.fr/IMG/pdf/dgos_atlas_sih_2018.pdf.
Barney, Jay. 1991. “Firm Resources and Sustained Competitive Advantage.” Journal of Management 17(1): 99–120.
Barney, Jay B. 1991. Firm Resources and Sustained Competitive Advantage. Rochester, NY: Social Science Research Network. SSRN Scholarly Paper. https://papers.ssrn.com/abstract=1505199 (March 10, 2019).
Bledow, Ronald et al. 2009. “A Dialectic Perspective on Innovation: Conflicting Demands, Multiple Pathways, and Ambidexterity.” Industrial and Organizational Psychology 2(3): 305–37.
Buchanan, David, Louise Fitzerald, and Diane Kelley. 2007. The Sustainability and Spread of Organizational Change: Modernizing Healthcare. https://www.crcpress.com/The-Sustainability-and-Spread-of-Organizational-Change-Modernizing-Healthcare/Buchanan-Fitzgerald-Ketley/p/book/9780415370950 (March 10, 2019).
Califf, Robert M. et al. 2016. “Transforming Evidence Generation to Support Health and Health Care Decisions.” New England Journal of Medicine 375(24): 2395–2400.
Christensen, Clayton M., Michael E. Raynor, and Rory McDonald. 2015. “What Is Disruptive Innovation?” Harvard Business Review (December 2015). https://hbr.org/2015/12/what-is-disruptive-innovation (March 10, 2019).
Coiera, Enrico. 2011. “Why System Inertia Makes Health Reform so Difficult.” BMJ 342: d3693.
Davies, Huw, Alison Powell, and Sandra Nutley. 2016. “Mobilizing Knowledge in Health Care.” The Oxford Handbook of Health Care Management. http://www.oxfordhandbooks.com/view/10.1093/oxfordhb/9780198705109.001.0001/oxfordhb-9780198705109-e-17 (March 10, 2019).
Dougherty, Deborah. 2004. “Organizing Practices in Services: Capturing Practice-Based Knowledge for Innovation.” Strategic Organization 2(1): 35–64.
Dougherty, Deborah, and Danielle D. Dunne. 2011. “Organizing Ecologies of Complex Innovation.” Organization Science 22(5): 1214–23.
Dzau, Victor J., Ziggy Yoediono, William F. ElLaissi, and Alex H. Cho. 2013. “Fostering Innovation in Medicine and Health Care: What Must Academic Health Centers Do?” Academic Medicine 88(10): 1424.
Ferlie, Ewan, Louise Fitzgerald, Martin Wood, and Chris Hawkins. 2005. “The Nonspread of Innovations: The Mediating Role of Professionals.” Academy of Management Journal: 19.
Hwang, Jason, and Clayton M. Christensen. 2008. “Disruptive Innovation in Health Care Delivery: A Framework for Business-Model Innovation.” Health Affairs (Project Hope) 27(5): 1329–35.
Lancet, The. 2018. “Standing by France’s Social Contract: Macron’s Health Reform.” The Lancet 392(10153): 1090.
Le CHU de demain: symbiose, créativité et excellence. 2018. Conférences des CHU. https://www.reseau-chu.org/article/chu-de-demain-5-axes-et-23-propositions/.
Ma Santé 2022: un engagement collectif. 2018. Ministère des Solidarités et de la Santé. https://solidarites-sante.gouv.fr/actualites/presse/dossiers-de-presse/article/dossier-de-presse-ma-sante-2022-un-engagement-collectif.
May, Carl. 2013. “Agency and Implementation: Understanding the Embedding of Healthcare Innovations in Practice.” Social Science & Medicine (1982) 78: 26–33.
Paulus, Ronald A., Karen Davis, and Glenn D. Steele. 2008. “Continuous Innovation In Health Care: Implications Of The Geisinger Experience.” Health Affairs 27(5): 1235–45.
Safon, Marie-Odile. 2017. Les réformes hospitalières en France : aspects historiques et réglementaires. IRDES.
Employees’ acceptance of disruptive digital service innovations in health care
Christian-Albrechts-Universität zu Kiel, Germany
As a part of digitalization, services are changing radically especially in health care. E.g., the use of artificial intelligence allows the automation of services and extent customer self-service concepts. Hence, employees may perceive digital change as a threat and may act as opponents, especially if the change requires substantial learning.
Our research takes place in the audiology sector, exemplary for many sectors in which automated solutions replace personal services (Huang and Rust, 2018). The audiology is sensitive for changes due to knowledge intensity and an intense interaction between frontline employees and customers (Ardolino et al., 2016). Changes can be perceived as threatening leading to employees holding onto their routines. We distinguish between competence destroying (disruptive) and competence enhancing service innovations. Disruptive services can influence employees’ adoption intention negatively. The potential to substitute competences of employees will be consequently perceived as a threat (van Hootegem et al., 2019).
Moreover we regard the influence of the innovation’s source on the acceptance. We distinguish between peer (acoustician) and manufacturer (hearing aid producers) driven innovations, presenting different threat levels as manufacturer may enter the market. The “not-invented-here syndrome” may lead to a decreasing acceptance of manufacturer driven innovation (De Araújo Bucharth et al., 2014).
The reaction of employees to potentially disrupting digital service innovations is of highest relevance but is hardly been addressed in innovation research. It is in particular interesting to explore interaction effects of the degree of potential disruption and the source of this potential threat.
How does the source of innovation influence a service employee’s acceptance of disruptive service innovations? How do employee and organizational characteristics affect the perceived level of threat of disruptive innovations? Which individual and organizational attributes may compensate potential negative perceptions of disruptive innovations that are launched by market entrants?
In our survey of acousticians we chose a 2x2 vignette experimental design testing their reaction to two future service scenarios and two opposite sources of the proposed service innovations. Each participant received the same fictitious text only differing in the informant’s origin describing possible outcomes of digitalization in audiology. To manipulate the service scenario we introduced different digital services that differ in their ability to substitute the current work. Furthermore, we measure personal and organizational factors that could have a further influence on the acceptance of digital services.
We survey more than 400 persons. The participants are audiologists, active for several years in practice, which are currently enrolled in a training program (master course) of the biggest education institution for acousticians in Germany. They are answering a ten-page questionnaire with questions regarding their personal innovativeness and digital knowledge, working environment (e.g. internal communication), organizational context (e.g. entrepreneurial orientation) and the intention to use different digital services. In this survey, we integrated a variation of digital services differing in their potential to substitute the current tasks of the employees. The services result of a larger qualitative study and analysis of current technology and market trends in audiology. In addition, 50% of the respondents were framed in the sense that the innovations are result of workshops and concrete development of audiologists (peers). The other half of the sample got the information that the sources of innovation are current activities by hearing aid manufactures. This leads to an experimental design of 2x2 groups (competence enhancing services/competence destroying services and acoustician as source of innovation/manufacturer as source of innovation). We apply ANOVA and multivariate regressions to analyze the data.
First results of the ongoing study suggest that the more a service innovation is perceived as disruptive, the less it will be accepted. As stated above, the potential to substitute the competence of employees leads to a perception of threat. Moreover an innovation is perceived more negatively if it doesn’t come from within the own profession. That means that the participants receiving a fictional scenario from the perspective of manufacturers accept the service innovations less. We also find an interaction effect that indicates that employees perceive disruptive service innovations as a threat if they are launched by manufactures. As a consequence barriers of adoption will arise which must be dissolved by the management.
On the personal level an employee’s degree of innovativeness and information capacities reduces the level of perceived threat and also reduces the negative effect of the not-invented-here syndrome. Similarly, an innovative organization culture exerts a positive influence on the perception of disruptive innovations. A culture supporting open internal communication leads to higher acceptance of changes, because the transparency of information can help understanding the necessity of digital innovations (Baer and Frese, 2003).
Contribution to Scholarship
The purpose of our study is to explain the factors, which influence the acceptance of radical changes in service organizations regarding the special case of health care organizations. Our results show that acceptance can be explained by various factors. On the individual level personal innovativeness, digital competence and personal characteristics like age or seniority may exert an influence. On the organizational level competitive intensity, internal communication, entrepreneurial orientation and organizational factors like firm size or leadership style are meaningful. Furthermore, the variation of service innovations (competence enhancing vs. destroying changes) and the source of innovation are direct predictors of employees’ acceptance of digital changes. The experimental design promises interesting insights into the backgrounds of acceptance and finally the adoption of disruptive service innovations by employees. Since we focus our research on frontline employees, we provide valuable insights on the micro foundation of digital innovation.
Contribution to Practice
We give advice how service firms can motivate employees to support organizational changes, especially within the context of digitalization. We describe the competences employees need to participate in digital innovation projects and how different personal traits affect the acceptance of disruptive digital service innovation. On the organizational level, communication and leadership strategies have to address the needs of employees and their perceived threat to allow an innovative behavior despite of a potential individual fear of change. The results that are gathered within the field of audiology can be transferred to other services and industries.
With our research, we are able to contribute to the investigation of barriers rising out of a company's social system as well as attitudes and learned behavior of its members. Especially radical technological changes may influence employees’ acceptance negatively, that's why we concentrate on potential consequences of individual threats.
Ardolino, Marco; Saccani, Nicola; Gaiardelli, Paolo; Rapaccini, Mario (2016): Exploring the Key Enabling Role of Digital Technologies for PSS Offerings. In: Procedia CIRP 47, p. 561–566. DOI: 10.1016/j.procir.2016.03.238.
Baer, Markus; Frese, Michael (2003): Innovation is not enough. Climates for initiative and psychological safety, process innovations, and firm performance. In: J. Organiz. Behav. 24 (1), p. 45–68. DOI: 10.1002/job.179.
De Araújo Bucharth, Ana L.; Praest Knudsen, Mette; Alsted Søndergaard, Helle (2014): Neither invented nor shared here: The impact and management of attitudes for the adoption of open innovation practices. In: Technovation 34 (3), p..149-161.
Huang, Ming-Hui; Rust, Roland T. (2018): Artificial Intelligence in Service. In: Journal of Service Research 21 (2), p. 155–172. DOI: 10.1177/1094670517752459.
van Hootegem, Anahí; Niesen, Wendy; Witte, Hans de (2019): Does job insecurity hinder innovative work behaviour? A threat rigidity perspective. In: Creat Innov Manag 28 (1), p. 19–29. DOI: 10.1111/caim.12271.
E-health innovations: what do we learn from the implementation of the surgical robot in operating rooms?
Université de Lorraine, France
The context of our research is an operating room using robotic surgery.
The surgical robot device has three elements connected by cables: carriage-patient, video column and the console. The computer-generated electrical impulses are transmitted by a 10-meter long cable that controls the articulated robot arms where articulated instruments are attached.
While the robot itself is very expensive, some studies highlight its advantages in global costs cuts (Defortescu G. & al., 2016; Pugin & al., 2011). For that, technical skills are needed as manual dexterity and eye-hand coordination (Agha & al., 2015; Hove & al. 2010). Results also depend on non-technical skills as situational awareness, decision making, communication, teamwork and leadership, and on the ways to overtake obstacles to communication: internal ones as difference of language, culture, motivation, expectations, past experience, status, feelings, and external ones as noise, little carrying voice, deafness, electric interference, spatiotemporal separation, lacking visual marks (Yule & Paterson-Brown, 2012). The literature on those questions is a big step for the healthcare management field but is too focused on the technical performance of the surgeon, on the training of the future surgeons or on the assessment of the technical and non-technical skills (Hull & al., 2012).
The literature focuses mostly on the hard factors of Operating Rooms (OR) optimizations and less on soft factors as change management and team. Studying soft factors of health innovation at the team level is an important research gap, especially in OR that are known to be more hierarchical than other hospital units.
We raise several research questions: What are the difficulties and impacts of implementing the surgical robot in an operating room? What are the ways to improve teamwork and communication in this new context? What does this implementation experience teach us for further e-health innovations?
We used a qualitative methodology developed in a pragmatist approach (Lorino, 2016). More specifically, we build a case study of an operating room using robotic surgery based on several methodological tools (Yin, 2008): observations, video-recording and self-confrontation interviews.
Video collecting gives a precise track of the surgical activity, which makes it possible to transcribe the verbal interactions but also to describe nonverbal interactions which take place during the intervention, and is a base for the self-confrontation interviews.
The self-confrontation interviews (adapted from Clot & al., 2000) make it possible to come back to the course of the activity.
Our case study is based on observations, on a video-recording and on self-confrontation interviews.
Observations took place at the very beginning of our research for better understanding the context of the members of the OR team, and the place and the role of each one in the team. Then observations were made for preparing the future video-recording to come. Finally observations were made during video recording to anticipate the analysis of this one by identifying keys-moments of the intervention.
A 3-hour long surgical intervention has been video-recorded and analyzed.
To complete the observations and the video-recording, self-confrontation interviews with each member of the surgical team were conducted and a restitution was made, followed by a discussion on the results.
Some other empirical materials are envisaged as observations of non-robotic surgery (open surgery and simple laparoscopy surgery) to isolate the effects of the surgical robot on the OR context and functioning, and observations and interviews in different specialization surgery than urology.
The surgical robot has an impact on the configuration, the functioning, the communication and the decision process within operating rooms and working teams.
The obstacles to communication and to teamwork are amplified by the spatial and temporal separation observed in and outside OR.
Some staff members are reluctant to use the surgical robot because it has an impact on the place they have in the team and the sense they make of their work.
Besides there is a kind of paradox: being a forerunner in the use of the surgical robot and in robotics training, seems to lead the actors to rely on their skills rather than to seek to improve the OR practices, through the training of new personnel and briefing/debriefing techniques for example.
Improving communication and teamwork in the context of robotic-assisted surgery is primarily based on the awareness of the obstacles presented above, the development of collective non-technical skills, the transformation of OR practices and the co-construction of management tools. For this, the creation of discussion spaces (Detchessahar & al., 2015) is an interesting track. For these spaces being effective and producing results, the institution must be looped and commit to supporting the team.
Contribution to Scholarship
As we saw, there are few publications in the healthcare management field on soft factors in OR context. Our research which tries to understand difficulties and impacts for the team of implementing the surgical robot in an operating room helps to address this gap.
Besides the literature identifies the main non-technical skills and communication obstacles and proposes some assessment grids. However there are few researches on how those skills and obstacles concretely express in an OR context using the surgical robot, and there is no such research conducted on the team level.
Indeed our research allowed us to analyze more precisely these phenomena by taking into account the interactions between all the actors working in the operating room – obviously the surgeon and the assistant but also the scrub and circulating nurses the anesthesiologists and the anesthetic nurses - and not only between surgeons as it is often the case.
Contribution to Practice
Several contributions to practice can be highlighted.
First of all we allow a better understanding on the obstacles to communication and team work in the context of an OR using robotic surgery.
Secondly, we propose some ways to improve teamwork and communication in this new context.
Finally our results could give some ideas to improve the technological device itself to be easier to appropriate.
Some of our results and recommendations can be useful for further e-health innovations and R&D management. Indeed, we show in a general way that thinking immediately at the team level and about the development of collective soft skills are very important when implementing technological innovation.
Agha, R.A., Fowler, A.J., Sevdalis, N. (2015). The role of non-technical skills in surgery. Annals of Medicine and Surgery 4, 422–427.
Clot, Y., Faïta, D., Fernandez, G., et Scheller, L. (2000). Entretiens en autoconfrontation croisée : une méthode en clinique de l’activité. Perspectives interdisciplinaires sur le travail et la santé (2-1).
Defortescu G., Tillou X. (2016), La chirurgie robot-assistée en transplantation rénale, Progrès en Urologie – FMC, 26:F67–F72.
Detchessahar M., Gentil S., Grevin A., Stimec A., 2015, Quels modes d’intervention pour soutenir la discussion sur le travail dans les organisations ? Réflexions méthodologiques à partir de l’intervention dans une clinique, @GRH 2015/3 (n°16), p. 63-89.
Hove P.D. (van), G.J. Tuijthof, E.G. Verdaasdonk, L.P. Stassen, Dankelman (2010), Objective assessment of technical surgical skills, Br. J. Surg. 97 (7).
Hull L., Arora S., Aggarwal R., Darzi A., Vincent C., Sevdalis N. (2012), “The impact of nontechnical skills on technical performance in surgery: a systematic review”, J. Am. Coll. Surg. 214, 214–230.
Lorino, P. (2016), L’apport de la pensée pragmatiste à l’approche processuelle. In: Théories des organisations. Nouveaux tournants. Paris: Economica, F.X. de Vaujany, A. Hussenot, J.F. Chanlat . p. 279-298.
Pugin F., Bucher P., Morel P. (2011), Histoire de la chirurgie robotique : d’AESOP à Da Vinci® en passant par Zeus®, Journal de Chirurgie Viscérale,148S, S3-S8.
Yin R.K. (2008). Case Study Research. Design and Methods, Thousand Oaks: Sage, 4th ed.
Yule S. et Paterson-Brown S. (2012), Surgeons’ Non-technical Skills, Surg Clin N Am 92, 37–50.